Systems and methods for presenting image classification results

ABSTRACT

An apparatus for performing image searches including a camera, storage devices storing a set of instructions, and a processor coupled to the at least one storage device and the camera. The instructions configure the at least one processor to perform operations including identifying attributes of the captured image using a classification model; identifying first results based on the identified attributes; selecting a subset of first results based on corresponding probability scores, generating a first graphical user interface including interactive icons corresponding to first results in the subset, an input icon, and a first button. The operations may also include receiving a selection of the first button, performing a search to identify second results, and generating a second graphical user interface displaying the second results.

The present disclosure relates generally to systems and methods forclassifying images and, more particularly, to systems and methods forclassifying images captured with portable devices and generatinggraphical user interfaces in the portable devices for filteringclassification results and/or providing feedback to image identificationor classification models.

BACKGROUND

Image classification models may be used to classify or categorize imagesbased on their parameters and/or extracted attributes. Using machinelearning methods, such as convolutional neural networks (CNNs), it ispossible to categorize images. For example, images may be manipulated tobe run in a CNN to either recognize or classify the images. Further,CNNs or—similar machine learning methods—may be employed to extractimage parameters that are then correlated with other parameters orattributes for classification. These methods can be powerful to identifyor categorize images because the correlations can be highly accurate.Moreover. CNNs may be updated or retrained frequently to accuratelycapture image parameters.

However, sharing or displaying results of an image classification modelcan be challenging. Frequently, a classification model returns multiplepotential classification results based on a sample image, with each oneof the results being associated with a different confidence level. Insome embodiments, only the result with highest confidence or score maybe presented to the user. The highest confidence result may,nonetheless, be inaccurate (e.g., the model is not properly tuned) ormay be undesirable to the user. Hence, as an alternative, multipleclassification results may be presented for the user's review. However,limited screen space in portable devices and lower resolutions mayresult in a review process that is tedious and difficult. In particular,users of portable devices may be required to scroll through multipledifferent classification options to review and analyze differentresults. Further, reviewing multiple classification options in portabledevices may clutter the limited screen space, making it difficult toidentify or select an adequate result. Alternatives of displayingisolated results in independent windows may result in poor userexperience because users would be required to scroll through differentgraphical user interfaces (GUIs) to identify a matching result.Moreover, because portable devices are normally subject to data caps andreduced bandwidths, in many situations it is impracticable to quicklysend the results to a portable device.

The disclosed systems and methods for generating graphical userinterfaces address one or more of the problems set forth above and/orother problems in the prior art.

SUMMARY

One aspect of the present disclosure is directed to an apparatus forperforming image searches. The apparatus may include a camera, at leastone storage device storing a set of instructions, and at least oneprocessor coupled to the at least one storage device and the camera. Theinstructions in the storage device may configure the at least oneprocessor to perform operations comprising: capturing an image by thecamera, identifying attributes of the captured image using aclassification model, identifying first results based on the identifiedattributes (the first results being associated with probability scores),selecting a subset of first results based on corresponding probabilityscores (the first results in the in the subset having an accumulatedprobability score greater than a threshold probability score), andgenerating a first graphical user interface. The first graphical userinterface may include interactive icons corresponding to first resultsin the subset, at least one input icon, and a first button. Theoperations may also include receiving a selection of the first buttonand, upon receiving the selection of the first button, performing asearch to identify second results, where the second search is based onat least one of selected interactive icons or input in the at least oneinput icon, and generating a second graphical user interface displayingthe second results.

Another aspect of the present disclosure is directed a system forperforming image searches. The system may include one or more processorsand one or more storage devices storing instructions that, whenexecuted, configure the one or more processors to perform operations.The operations may include identifying attributes of a captured imagecaptured using a classification model, identifying first results basedon the identified attributes (the first results being associated withprobability scores), selecting a subset of the first results based oncorresponding probability scores (the first results in the subset havingan accumulated probability score greater than a threshold probabilityscore), and generating a first graphical user interface. The firstgraphical user interface may include interactive icons corresponding tofirst results in the subset, at least one input icon, and a button. Theoperations may also include, upon receiving a selection of the button,performing a search to identify second results (the second search beingbased on at least one of selected interactive icons or input in the atleast one input icon) and generating a second graphical user interfacedisplaying the second results.

Yet another aspect of the present disclosure is directed to acomputer-implemented method for performing image searches. The methodmay include identifying attributes of a captured image using aclassification model; identifying first results based on the identifiedattributes (the first results being associated with probability scores),selecting a subset of the first results based on correspondingprobability scores (the first results in the subset having anaccumulated probability score greater than a threshold probabilityscore), and generating a first graphical user interface. The firstgraphical user interface may include interactive icons corresponding tofirst results in the subset, at least one input icon, and a firstbutton. The operations may also include, upon receiving a selection ofthe first button, performing a search to identify second results (thesecond search being based on at least one of selected interactive iconsor input in the at least one input icon) and generating a secondgraphical user interface displaying the second results.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate disclosed embodiments and,together with the description, serve to explain the disclosedembodiments. In the drawings:

FIG. 1 is a block diagram of an exemplary system, consistent withdisclosed embodiments.

FIG. 2 is a block diagram of an exemplary image recognizer, consistentwith disclosed embodiments.

FIG. 3 is a block diagram of an exemplary model generator, consistentwith disclosed embodiments.

FIG. 4 is a block diagram of an exemplary database, consistent withdisclosed embodiments.

FIG. 5 is a block diagram of an exemplary client device, consistent withdisclosed embodiments.

FIG. 6 is a flowchart illustrating an exemplary process for thegeneration of graphical user interfaces, consistent with disclosedembodiments.

FIG. 7 is a flowchart illustrating an exemplary process for modifyinggraphical user interfaces, consistent with disclosed embodiments.

FIG. 8 is a flow chart illustrating an exemplary server query andsecondary search process, consistent with disclosed embodiments.

FIG. 9 is a flow chart illustrating an exemplary process forrepopulating primary results, consistent with disclosed embodiments.

FIG. 10 is a flow chart illustrating an exemplary client queryresolution process, consistent with disclosed embodiments.

FIG. 11 is a flowchart illustrating an exemplary process for generatingan identification model, consistent with disclosed embodiments.

FIG. 12 is a flow chart illustrating an exemplary patch generationprocess, consistent with disclosed embodiments.

FIG. 13 is a flow chart illustrating an exemplary process for selectingresults displayed in a graphical user interface, consistent withdisclosed embodiments.

FIG. 14 is a process flow illustrating an exemplary communicationsequence, consistent with disclosed embodiments.

FIG. 15 is an exemplary first graphical user interface, according withdisclosed embodiments.

FIG. 16 is an exemplary second graphical user interface showingpreliminary results and filter options, according with disclosedembodiments.

FIG. 17 is an exemplary third graphical user interface showingpreliminary results and user input boxes, according with disclosedembodiments.

FIG. 18 is an exemplary fourth graphical user interface showingclassification results, according with disclosed embodiments.

FIG. 19 is an exemplary fifth graphical user interface showing anotification message, according with disclosed embodiments.

FIG. 20 is an exemplary sixth graphical user interface showing an errornotification message, according with disclosed embodiments.

DETAILED DESCRIPTION

The disclosure is generally directed to systems and methods to generategraphical user interfaces (GUIs). The disclosed systems and methods mayimprove user experience when employing image classification models bygenerating GUIs that allow users to filter, prioritize, and/or providefeedback to image identification models. For example, the disclosedsystems and methods may generate intermediary GUIs that display filtersand/or preselected results after an image is captured. Alternatively, oradditionally, the intermediary GUIs may be displayed after a userselects or uploads a sample image for an image classification. In someembodiments, the intermediary GUIs may show preliminary results and/orinteractive icons with filtering options that may reduce the number ofpotential image classification results. Thus, intermediary GUIs of thedisclosed systems and methods may reduce bandwidth consumed during imageclassification and improve user experience by reducing the number ofresults a user needs to review.

Further the disclosed systems and methods may be employed to effectivelymanage screen space. Particularly for portable devices, in which thescreen space is limited, the disclosed systems and methods may generatedynamic GUIs that specifically display elements that are relevant for auser search while removing irrelevant elements. For example, thedisclosed methods may include dynamically modifying GUIs based on userselections to maximize screen space by eliminating, minimizing,enlarging, hiding, and/or re-shaping icons based on user selections.Further, to improve GUIs in portable screens, the disclosed systems andmethods may determine the number of results displayed based onidentification confidence levels and/or user visualization preferences.These GUI configurations may facilitate performing accurate imageclassification by displaying preliminary classification results to focususer attention in relevant results and showing interactive filters andoptions that prevent searching for irrelevant images or selectinginaccurate results.

Moreover, the disclosed systems and methods may be used to generate GUIswith interactive elements to easily communicate with, and providefeedback tot image recognizers. For example, in some embodiments of thedisclosed systems, image recognizer may employ machine-learning models,such as convolutional neural networks (CNNs) to identify or classifyimages. The disclosed systems and methods may provide interactive GUIsthat allow users to quickly provide feedback to the models. For example,GUIs generated with the disclosed systems and methods may allow users toindicate when classification or identification models are returninginaccurate results. Further, the disclosed systems and methods mayenable users to provide corrected results and labeled images, which canbe used to retrain classification models and improve the model accuracy.Thus, the disclosed systems and methods may facilitate capturing samplesfor training datasets used to update identification models.

In such embodiments, the disclosed systems and methods may be employedto leverage user feedback when generating software patches for imageclassification models. For example, information captured with GUI'sconfigured to gather user input may be used to create patches (i.e., aset of changes to a computer program or its supporting data designed toupdate, fix, or improve it) for identification or classification models.The patches may create model exceptions based on user inputs when thesystem identifies trends of model inaccuracies. Alternatively, oradditionally, the patches may modify parameters of the model to correctinaccuracies and/or improve model configurations.

The disclosed systems and methods may also improve computerfunctionality by distributing search tasks between different elements ofthe system. For example, in the disclosed systems and methods someinitial image classification operations may be performed locally in aportable device using machine-learning models like CNNs. This initialsearch may filter content for a second search that involvescommunication with a server and may include commercial and/or financingconsiderations. By distributing tasks at different points of the systemarchitecture, the disclosed systems and methods may reduce networkcongestion while also allowing the identification of preliminarysearches. In such embodiments, the disclosed systems and methods mayalso modify server query information so it can be easily transmitted innetworks with limited bandwidth, such as cellular networks. For example,the disclosed systems and methods may create packets of information totransmit captured images that have modified metadata to facilitatesearches and/or allow retraining of the identification models. Further,the use of distributed systems with different search tasks improveoperation of portable computing devices because it allows reducingbandwidth required of identification.

Some embodiments of the disclosed systems and methods may be tailored toimprove the technical field of identifying images of vehicles. Imageidentification of vehicles may be particularly useful when using mobiledevices because it enables the combination of multiple capabilitiescurrently available in portable devices. For example, the disclosedsystems and methods may combine image identification, using a camera andcomputing power of a smartphone, with native communication and locationfunctions to provide targeted content for the user. In such embodiments,a user may use an image classification process to identify a vehicle inthe street. Based on the image classification results and the locationof the user, a server may push notifications or information about theautomobile and dealers in proximity to the user. Accordingly, thedisclosed systems and methods may improve the technical field ofrecognizing vehicle images by coordinating communication betweendifferent elements of the system, reducing latency between thecommunications, reducing bandwidth utilization, and improving accuracyand relevance of the results presented to the user.

Moreover, the disclosed system and methods may improve the technicalfield of augmented reality in vehicle identification. The disclosedsystems and methods may be employed to generate augmented realityimages. For example, results from image classification in the disclosedsystems and methods may be used to modify video feeds to include vehicleinformation and generate augmented reality experiences. Thus, the usermay quickly identify vehicles in the user screen based on imageclassification processes. Modifications to images in the video feed mayinclude displaying identified features of vehicles in the screen anddisplay inventory information available in servers coupled to thedevice. Such arrangement for augmented reality may improve userexperience because it may enable users to quickly identity features ofautomobiles the user is looking at, with minimal required input from theuser.

Reference will now be made in detail to the disclosed embodiments,examples of which are illustrated in the accompanying drawings.

FIG. 1 is a block diagram of an exemplary system 100, consistent withdisclosed embodiments. System 100 may be used to support imagerecognition in portable devices and for communication of targetedcontent to portable devices, consistent with disclosed embodiments.System 100 may include an inventory search system 105 which may includean image recognizer 110 and a model generator 120. System 100 mayadditionally include online resources 140, client devices 150, computingclusters 160, and databases 180. In some embodiments, as shown in FIG. 1, components of system 100 may be connected to a network 170. However,in other embodiments components of system 100 may be connected directlywith each other, without network 170.

Online resources 140 may include one or more servers or storage servicesprovided by an entity such as a provider of website hosting, networking,cloud, or backup services. In some embodiments, online resources 140 maybe associated with hosting services or servers that store web pages forvehicle manufacturers and/or vehicle dealers. Alternatively, oradditionally, online resources 140 may be associated with serviceproviders such, such as financing service providers. In suchembodiments, online resources 140 may be configured to determineavailability of financial services for users based on query request andinformation stored in databases 180. Moreover, online resources 140 mayalso be associated with website aggregators, such as Kelley Blue Book®,a or cloud computing service. In yet other embodiments, online resources140 may be associated with a messaging service, such as, for example,Apple Push Notification Service, Azure Mobile Services, or Google CloudMessaging. In such embodiments, online resources 140 may handle thedelivery of messages and notifications related to functions of thedisclosed embodiments, such as image compression, notification ofidentified vehicles alerts, and/or completion messages andnotifications. In some embodiments, online resources 140 may includeservers associated with both vehicle dealers and finance services forvehicle purchases. In such embodiments, online resources 140 may storevehicle inventory information (including price, location, andavailability) and also store information about client accounts and theircredit history.

Client devices 150 may include one or more computing devices configuredto perform one or more operations consistent with disclosed embodiments.For example, client devices 150 may include a desktop computer, alaptop, a server, a mobile device (e.g., tablet, smart phone, etc.), agaming device, a wearable computing device, or other type of computingdevice. Client devices 150 may include one or more processors configuredto execute software instructions stored in memory, such as memoryincluded in client devices 150, to perform operations to implement thefunctions described below. Client devices 150 may include software thatwhen executed by a processor performs Internet-related communication,such as TCP/IP, and content display processes. For instance, clientdevices 150 may execute browser software that generates and displaysinterfaces including content on a display device included in, orconnected to, client devices 150.

Client devices 150 may execute applications that allow client devices150 to communicate with components over network 170, and generate anddisplay content in GUIs via display devices included in client devices150. Displays of client devices 150 may be configurable to display GUIsas described in connection with FIGS. 15-20. The disclosed embodimentsare not limited to any particular configuration of client devices 150.For instance, a client device 150 may be a mobile device that stores andexecutes mobile applications to perform operations that providefunctions offered by inventory search system 105 and/or online resources140, such as providing information about vehicles in a database 180. Incertain embodiments, client devices 150 may be configured to executesoftware instructions relating to location services, such as GPSlocations. For example, client devices 150 may be configured todetermine a geographic location and provide location data and time stampdata corresponding to the location data. Alternatively, or additionally,client devices 150 may have camera 520 to capture video and/or images.Client devices 150 are further described in connection with FIG. 5 .

Computing clusters 160 may include a plurality of computing devices incommunication with other elements of system 100. For example, in someembodiments, computing clusters 160 may be a group of processors incommunication through fast local area networks. In other embodimentscomputing clusters 160 may be an array of graphical processing unitsconfigured to work in parallel as a graphics processing unit (GPU)cluster. In such embodiments, computer clusters 160 may includeheterogeneous or homogeneous hardware. In some embodiments, computingclusters 160 may include GPU drivers for the various types of GPUspresent in cluster nodes, a Clustering Application Programing Interface(API), such as the Message Passing Interface (MPI), and VirtualCL (VCL)cluster platform such as a wrapper for OpenCL™ that allows applicationsto transparently utilize multiple OpenCL devices in a duster. In yetother embodiments, computing clusters 160 may operate with distcc (aprogram to distribute builds of C. C++, Objective C or Objective C++code across several machines on a network to speed up building), andMPICH (a standard for message-passing for distributed-memoryapplications used in parallel computing), Linux Virtual Server™,Linux-HA™, or other director-based clusters that allow incoming requestsfor services to be distributed across multiple cluster nodes.

In some embodiments, computer clusters 160 may receive requests togenerate models. For example, computer clusters 160 may receive a modelrequest from model generator 120 including training data, which mayinclude images of vehicles with labeled metadata. Computer clusters 160may perform the multiple iterations for generating a classification oridentification model based on the training data. For example, computerclusters 160 may perform operations to generate a convolutional neuralnetwork that identifies attributes in images of vehicles.

Databases 180 may include one or more computing devices configured withappropriate software to perform operations consistent with providinginventory search system 105, model generator 120, and image classifier130 with data associated with vehicle images, vehicle features, andstored information about vehicle sales like cost or condition. Databases180 may include, for example, Oracle™ databases, Sybase™ databases, orother relational databases or non-relational databases, such as Hadoop™sequence files, HBase™, or Cassandra™. Database(s) 180 may includecomputing components (e.g., database management system, database server,etc.) configured to receive and process requests for data stored inmemory devices of the database(s) and to provide data from thedatabase(s).

While databases 180 are shown separately, in some embodiments databases180 may be included in or otherwise related to one or more of inventorysearch system 105, image recognizer 110, model generator 120, imageclassifier 130, and online resources 140.

Databases 180 may be configured to collect and/or maintain the dataassociated with vehicles being displayed in online resources 140 andprovide it to the inventory search system 105, model generator 120,image classifier 130, and client devices 150. Databases 180 may collectthe data from a variety of sources, including, for instance, onlineresources 140. Databases 180 are further described below in connectionwith FIG. 4 .

Model generator 120 may include one or more computing systems configuredto generate models to classify or identify images. Model generator 120may receive or obtain data from databases 180, computing clusters 160,and/or online resources 140. For example, model generator 120 mayreceive a plurality of images from databases 180, online resources 140,and/or client devices 150. Alternatively, or additionally, modelgenerator 120 may employ a web scraper to collect images of vehiclesfrom elements of system 100. For example, model generator 120 may scrapeimages of vehicles from online resources 140 and/or client devices 150.In some embodiments, the images received or collected by model generator120 may be images of vehicles. In such embodiments, model generator 120may filter the images to eliminate images of vehicle interiors to leaveonly images of vehicle exteriors. Model generator 120 may label thecollected or received images with metadata that identify vehiclecharacteristics, such as model, make or trim, but in some embodimentsthe images collected or received may already include identifyingmetadata. Model generator 120 may also receive images and metadata fromimage recognizer 110.

In some embodiments, model generator 120 may receive requests from imagerecognizer 110. As a response to the request, model generator 120 maygenerate one or more classification models or image identificationmodels. Classification models may include statistical algorithms thatare used to determine the likeliness between images given a set oftraining images. For example, classification models may be convolutionalneural networks (CNNs) that determine attributes in a figure based onextracted parameters. However, identification models may also includeregression models that estimate the relationships among input and outputvariables. Identification or classification models may additionally sortelements of a dataset using one or more classifiers to determine theprobability of a specific outcome. Identification or classificationmodels may be parametric, non-parametric, and/or semi-parametric models.

In some embodiments, classification or identification models mayrepresent an input layer and an output layer connected via nodes withdifferent activation functions as in a convolutional neural network.“Layers” in the neural network may transform an input variable into anoutput variable (e.g., holding the class scores) through adifferentiable function. The CNN may include multiple distinct types oflayers. For example, the network may include a convolution layer, apooling layer, a ReLU Layer, a number filter layer, a filter shapelayer, and/or a loss layer. Further, the CNN may include a plurality ofnodes. The nodes may be associated with activation functions and may beconnected with other nodes via synapsis that are associated with aweight. The neural networks may model input/output relationships ofvariables and parameters by generating a number of interconnected nodesthat contain an activation function. The activation function of a nodemay define a resulting output of that node, given an argument or a setof arguments. Artificial neural networks may generate patterns to thenetwork via an ‘input layer,’ which may communicate to one or more“hidden layers” where the system determines regressions via weightedconnections. Identification and classification models may also includeRandom Forests, composed of a combination of decision tree predictors.(Decision trees may include data structure mapping observations aboutsomething, in the “branch” of the tree, to conclusions about thatthing's target value, in the “leaves” of the tree.) Each tree may dependon the values of a random vector sampled independently and with the samedistribution for all trees in the forest. Identification models mayadditionally or alternatively include classification and regressiontrees.

Model generator 120 may submit models to identify a vehicle. To generateidentification models, model generator 120 may analyze images collectedor received from other elements of system 100, applying machine-learningmethods. Model generator 120 is further described below in connectionwith FIG. 3 .

In some embodiments, model generator 120 may generate “patches,” thatis, modifications, for already developed classification oridentification models. Model generator 120 may generate a patch that mayinclude a set of changes to the identification or classification modelto update, fix, or improve the model. The patch may include fixingsecurity vulnerabilities and other bugs that cause inaccuracies. Thepatch generated by model generator 120 may also improve the usability orperformance. For example, the patch may be directed to improvinggraphical user interfaces and/or correct attribute extraction processes.Patching processes are further described in connection with FIG. 12 .

Image recognizer 110 may include one or more computing systemsconfigured to perform operations consistent with performing imageclassification and/or identifying images. For example, image recognizer110 may perform operations to perform image classification of vehiclesbased on an image captured by cameras in client devices 150. In someembodiments, image recognizer 110 may receive a request to identify animage. Image recognizer 110 may receive the request directly from clientdevices 150. Alternatively, image recognizer 110 may receive the requestfrom other components of system 100. For example, client devices 150 maysend requests to online resources 140, which then send requests toinventory search system 105. Requests may include images of vehicles andlocations of client devices 150. Additionally, in some embodiments,requests may specify dates and preferences. In other embodiments,requests may include video files or streaming video feeds.

As a response to identification requests, inventory search system 105may initiate identification models using model generator 120. Therequest may include information about the image source, for example anidentification of client device 150. The request may additionallyspecify a location. In addition, image recognizer 110 may retrieveinformation from databases 180.

Image recognizer 110 may generate an identification result based on theinformation received from client device 150 request and transmit theinformation to the client device. Image recognizer 110 may generateinstructions to modify a graphical user interface to includeidentification information associated with the received image. Imagerecognizer 110 is further described below in connection with FIG. 2 .

Although in FIG. 1 image recognizer 110 is shown as part of inventorysearch system 105, in some embodiments (not shown) image recognizer 110may be implemented by other elements of system 100. For example, imagerecognizer 110, or functions performed by image recognizer 110 may beperformed in client devices 150. Client devices 150 may execute imagerecognizer 110 functions and identify images using classification oridentification models.

Moreover. FIG. 1 shows image recognizer 110 and model generator 120, asdifferent components. However, image recognizer 110 and model generator120, may be implemented in the same computing system. For example, allelements in inventory search system 105 may be embodied in a singleserver. Alternatively, image recognizer 110 and model generator 120 maybe located in different portions of system 100 to improve communicationflows. For example, when image recognizer 110 is not implemented as partof client device 150 but a user desires to minimize latency, imagerecognizer 110 may be implemented in an edge server of inventory searchsystem 105 while model generator 120, which may be accessed lessfrequently, may be placed in a back-end server of inventory searchsystem 105.

Network 170 may be any type of network configured to providecommunications between components of system 100. For example, network170 may be any type of network (including infrastructure) that providescommunications, exchanges information, and/or facilitates the exchangeof information, such as the Internet, a Local Area Network, near fieldcommunication (NFC), optical code scanner, or other suitableconnection(s) that enables the sending and receiving of informationbetween the components of system 100. In other embodiments, one or morecomponents of system 100 may communicate directly through a dedicatedcommunication link(s). Further, network 170 may include a network ofnetwork, coordinating communication through several networks.

It is to be understood that the configuration and boundaries of thefunctional building blocks of system 100 have been defined herein forthe convenience of the description. Alternative boundaries can bedefined so long as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments.

FIG. 2 shows a block diagram of an exemplary image recognizer 110,consistent with disclosed embodiments. Image recognizer 110 may includea communication device 210, a recognizer memory 220, and one or morerecognizer processors 230. Recognizer memory 220 may include recognizerprograms 222 and recognizer data 224. Recognizer processor 230 mayinclude an image normalization module 232, an image feature extractionmodule 234, and an identification engine 236.

In some embodiments, image recognizer 110 may take the form of a server,a general-purpose computer, a mainframe computer, or any combination ofthese components. In other embodiments, image recognizer 110 may be avirtual machine. In yet other embodiments, operations and functionsdescribed for image recognizer 110 may be implemented by client devices150 and processing units in client devices 150. Other implementationsconsistent with disclosed embodiments are possible as well.

Communication device 210 may be configured to communicate with one ormore databases, such as databases 180 described above, either directly,or via network 170. In particular, communication device 210 may beconfigured to receive from model generator 120 a model to identifyvehicle attributes in an image and client images from client devices150. In addition, communication device 210 may be configured tocommunicate with other components as well, including, for example,databases 180.

Communication device 210 may include, for example, one or more digitaland/or analog devices that allow communication device 210 to communicatewith and/or detect other components, such as a network controller and/orwireless adaptor for communicating over the Internet. Otherimplementations consistent with disclosed embodiments are possible aswell.

Recognizer memory 220 may include one or more storage devices configuredto store instructions used by recognizer processor 230 to performfunctions related to disclosed embodiments. For example, recognizermemory 220 may store software instructions, such as recognizer program222, that may perform operations when executed by recognizer processor230. The disclosed embodiments are not limited to separate programs orcomputers configured to perform dedicated tasks. For example, recognizermemory 220 may include a single recognizer program 222 that performs thefunctions of image recognizer 110, or recognizer program 222 may includemultiple programs. Recognizer memory 220 may also store recognizer data224 that is used by recognizer program(s) 222.

In certain embodiments, recognizer memory 220 may store sets ofinstructions for carrying out processes to identify a vehicle from animage, generate a list of identified attributes, and/or generateinstructions to display a modified graphical user interface, describedbelow in connection with FIGS. 15-20 . In certain embodiments,recognizer memory 220 may store sets of instructions for identifyingwhether an image is acceptable for processing and generate instructionsto guide the user in taking an acceptable image. Other instructions arepossible as well. In general, instructions may be executed by recognizerprocessor 230 to perform processes consistent with disclosedembodiments.

In some embodiments, recognizer processor 230 may include one or moreknown processing devices, such as, but not limited to, microprocessorsfrom the Pentium™ or Xeon™ family manufactured by Intel™, the Turion™family manufactured by AMD™, or any of various processors from othermanufacturers. However, in other embodiments, recognizer processor 230may be a plurality of devices coupled and configured to performfunctions consistent with the disclosure.

In some embodiments, recognizer processor 230 may execute software toperform functions associated with each component of recognizer processor230. In other embodiments, each component of recognizer processor 230may be an independent device. In such embodiments, each component may bea hardware device configured to specifically process data or performoperations associated with modeling hours of operation, generatingidentification models and/or handling large data sets. For example,image normalization module 232 may be a field-programmable gate array(FPGA), image feature extraction module 234 may be a graphics processingunit (GPU), and identification engine 236 may be a central processingunit (CPU). Other hardware combinations are also possible. In yet otherembodiments, combinations of hardware and software may be used toimplement recognizer processor 230.

Image normalization module 232 may normalize a received image so it canbe identified in the model. For example, communication device 210 mayreceive an image from client devices 150 to be identified. The image maybe in a format that cannot be processed by image recognizer 110 becauseit is in an incompatible format or may have parameters that cannot beprocessed. For example, an image may be received in a specific formatsuch as High Efficiency Image File Format (HEIC) or in a vector imageformat such as Computer Graphic Metafile (CGM). Then, imagenormalization module 232 may convert the received image to a standardformat such as JPEG or TIFF. Alternatively, or additionally, thereceived image may have an aspect ratio that is incompatible with anidentification model. For example, the image may have a 2.39:1 ratio,that may be incompatible with the identification model. Then, imagenormalization module 232 may convert the received image to a standardaspect ratio such as 4:3. In some embodiments, the normalization may beguided by a model image. For example, a model image stored in recognizerdata 224 may be used to guide the transformations of the received image.

In some embodiments, recognizer processor 230 may implement imagenormalization module 232 by executing instructions to create anapplication in which images are received and transformed. In otherembodiments, however, image normalization module 232 may be a separatehardware device or group of devices configured to carry out imageoperations. For example, to improve performance and speed of the imagetransformations, image normalization module 232 may be an SRAM-basedFPGA which functions as image normalization module 232. Imagenormalization module 232 may have an architecture designed forimplementation of specific algorithms. For example, image normalizationmodule 232 may include a Simple Risc Computer (SRC) architecture orother reconfigurable computing system.

Image feature extraction module 234 may extract features from a receivedimage or a normalized image. In some embodiments, features may beextracted from an image by applying a pre-trained convolutional neuralnetwork. For example, in some embodiments pre-trained networks such asInception-v3, AlexNet, or TensorFlow may be used to automaticallyextract features from a target image. In such embodiments, featureextraction module 234 may import layers of a pre-trained convolutionalnetwork, determine features described in a target layer of thepre-trained convolutional network, and initialize a multiclass fittingmodel using the features in the target layer and images received forextraction.

In other embodiments, other deep learning models such as Fast R-CNN canbe used for automatic feature extraction. In yet other embodimentsprocesses such as histogram of oriented gradients (HOG), speeded-uprobust features (SURF), local binary patterns (LBP), color histogram, orHaar wavelets may also be used to extract features from a receivedimage. In some embodiments, image feature extraction module 234 maypartition the image in a plurality of channels and a plurality ofportions, such that the channels determine a histogram of imageintensities, determine feature vectors from intensity levels, andidentify objects in a region of interest. Image feature extractionmodule 234 may perform other techniques to extract features fromreceived images.

Recognizer processor 230 may implement image feature extraction module234 by executing software to create an environment for extracting imagefeatures. However, in other embodiments image feature extraction module234 may include independent hardware devices with specific architecturesdesigned to improve the efficiency of aggregation or sorting processes.For example, image feature extraction module 234 may be a GPU arrayconfigured to partition and analyze layers in parallel. Alternatively,or additionally, image feature extraction module 234 may be configuredto implement a programming interface, such as Apache Spark™, and executedata structures, cluster managers, and/or distributed storage systems.For example, image feature extraction module 234 may include a resilientdistributed dataset that is manipulated with a standalone softwareframework and/or a distributed file system.

Identification engine 236 may calculate correlations between a receivedimage and stored attributes based on one or more identification models.For example, identification engine 236 may use a model from modelgenerator 120 and apply inputs based on a received image or receivedimage features to generate an attributes list associated with thereceived image.

Identification engine 236 may be implemented by recognizer processor230. For example, recognizer processor 230 may execute software tocreate an environment to execute models from model generator 120.However, in other embodiments identification engine 236 may includehardware devices configured to carry out parallel operations. Somehardware configurations may improve the efficiency of calculations,particularly when multiple calculations are being processed in parallel.For example, identification engine 236 may include multicore processorsor computer clusters to divide tasks and quickly perform calculations.In some embodiments, identification engine 236 may receive a pluralityof models from model generator 120. In such embodiments, identificationengine 236 may include a scheduling module. The scheduling module mayreceive models and assign each model to independent processors or cores.In other embodiments, identification engine 236 may be FPGA Arrays toprovide greater performance and determinism.

The components of image recognizer 110 may be implemented in hardware,software, or a combination of both, as will be apparent to those skilledin the art. For example, although one or more components of imagerecognizer 110 may be implemented as computer processing instructionsembodied in computer software, all or a portion of the functionality ofimage recognizer 110 may be implemented in dedicated hardware. Forinstance, groups of GPUs and/or FPGAs maybe used to quickly analyze datain recognizer processor 230. Further, components of image recognizer 110may be implemented within client devices 150. For example, processors inclient devices 150 may implement image normalization module 232, imagefeature extraction module 234, and/or identification engine 236. In suchembodiments, client devices 150 may be used to generate augmentedreality images that super impose icons on video feeds captured by clientdevices like it is further described in connection with FIG. 15 .

Referring now to FIG. 3 , there is shown a block diagram of an exemplarymodel generator, consistent with disclosed embodiments. Model generator120 may include a model processor 340, a model memory 350, and acommunication device 360.

Model processor 340 may be embodied as a processor similar to recognizerprocessor 230. Model processor may include a model builder 346, a costfunction calculator 348, and an image filter 349.

Model builder 346 may be implemented in software or hardware configuredto create identification models based on training data. In someembodiments, model builder 346 may generate CNNs. For example, modelbuilder 346 may take a group of labeled vehicle images from databases180 to train a CNN. In some embodiments, model builder 346 may generatenodes, synapsis between nodes, pooling layers, and activation functions,to create a vehicle identification model. Model builder 346 maycalculate coefficients and hyperparameters of the convolutional neuralnetworks based on the training data set. In such embodiments, modelbuilder 346 may select and/or develop CNNs in a backpropagation withgradient descent. However, in other embodiments, model builder 346 mayuse Bayesian algorithms or clustering algorithms to generateidentification models. In this context, a “cluster” is a computationoperation of grouping a set of objects in such a way that objects in thesame group (called a cluster) are more similar to each other than tothose in other groups or clusters. In yet other embodiments, modelbuilder 346 may use association rule mining, random forest analysis,and/or deep learning algorithms to develop models. In some embodiments,to improve the efficiency of the model generation, model builder 346 maybe implemented in one or more hardware devices, such as FPGAs,configured to generate models for vehicle image identification. In someembodiments, model builder 346 may train a model using a group oflabeled images for an image classification task (e.g., images ofvehicles including make/model information).

Cost function calculator 348 may be implemented in software or hardwareconfigured to evaluate the accuracy of a model. For example, costfunction calculator 348 may estimate the accuracy of a model, generatedby model builder 346, by using a validation dataset. In someembodiments, the validation data set may be a portion of a training dataset that was not used to generate the identification model.Alternatively, or additionally, validation datasets may be built fromuser feedback that may include images and metadata with vehicleinformation as further described in connection with FIG. 12 . Costfunction calculator 348 may generate error rates for the identificationmodels and may additionally assign weight coefficients to models basedon the estimated accuracy.

Image filter 349 may be implemented in software or hardware configuredto generate additional images to enhance the training data set used bymodel builder 346. One challenge in implementing portable identificationsystems using CNNs is the lack of uniformity in the images received frommobile devices. This problem is particularly exacerbated for augmentedreality applications that have images in multiple angles, with varyingcontrast, and in movement. To enhance accuracy and prevent sending errormessages requesting the user to take and send new images, image filter349 may generate additional images based on images received fromdatabases 180 and/or client devices 150. For example, image filter 349may take an image and apply rotation, flipping, or shear filters togenerate new images that can be used to train the convolutional neuralnetwork. These additional images may improve the accuracy of theidentification model, particularly in augmented reality applications, inwhich the images may be tilted or flipped as the user of client devices150 takes images.

Model memory 350 may include one or more storage devices configured tostore instructions used by model processor 340 to perform operationsrelated to disclosed embodiments. For example, model memory 350 maystore software instructions, such as model program 352, that may performoperations when executed by model processor 340. In addition, modelmemory 350 may include model data 354, which may include images to traina convolutional neural network.

In certain embodiments, Model memory 350 may store sets of instructionsfor carrying out processes to generate a model that identifiesattributes of a vehicle, described below in connection with FIG. 11 .

Referring now to FIG. 4 , there is shown a block diagram of an exemplarydatabase 180 (FIG. 1 ), consistent with disclosed embodiments. Database180 may include a communication device 402, one or more databaseprocessors 404, and database memory 410 including one or more databaseprograms 412 and data 414.

In some embodiments, databases 180 may take the form of servers,general-purpose computers, mainframe computers, or any combination ofthese components. Other implementations consistent with disclosedembodiments are possible as well.

Communication device 402 may be configured to communicate with one ormore components of system 100, such as online resource 140, inventorysearch system 105, model generator 120, image recognizer 110, and/orclient devices 150. In particular, communication device 402 may beconfigured to provide to model generator 120 images of vehicles that maybe used to generate a CNN, an image classification model, an imageparameter extraction model, and/or an image identification model.

Communication device 402 may be configured to communicate with othercomponents as well, including, for example, model memory 352 (FIG. 3 ).Communication device 402 may take any of the forms described above forcommunication device 210 (FIG. 2 ).

Database processors 404, database memory 410, database programs 412, anddata 414 may take any of the forms described above for recognizerprocessors 230, memory 220, recognizer programs 222, and recognizer data224, respectively, in connection with FIG. 2 . The components ofdatabases 180 may be implemented in hardware, software, or a combinationof both hardware and software. For example, although one or morecomponents of databases 180 may be implemented as computer processinginstruction modules, all or a portion of the functionality of databases180 may be implemented instead in dedicated electronics hardware.

Data 414 may be data associated with websites, such as online resources140. Data 414 may include, for example, information relating to websitesof automobile dealers and/or automobile manufacturers. Data 414 mayinclude images of automobiles and information relating to automobiles,such as cost, condition, and dealers offering the automobile for sale.

Referring now to FIG. 5 , there is shown a block diagram of an exemplaryclient device 150 (FIG. 1 ), consistent with disclosed embodiments. Inone embodiment, client devices 150 may include one or more processors502, one or more input/output (I/O) devices 504, and one or morememories 510. In some embodiments, client devices 150 may take the formof mobile computing devices such as smartphones or tablets,general-purpose computers, or any combination of these components.Further, client devices 150 may take the form of wearable devices, suchas smart glasses or smartwatches. Alternatively, client devices 150 (orsystems including client devices 150) may be configured as a particularapparatus, embedded systems, dedicated circuit, and the like based onthe storage, execution, and/or implementation of the softwareinstructions that perform one or more operations consistent with thedisclosed embodiments. According to some embodiments, client devices 150may include web browsers or similar computing devices that access website consistent with disclosed embodiments.

Processor 502 may include one or more known processing devices, such asmobile device microprocessors manufactured by Intel™, NVIDIA™, orvarious processors from other manufacturers. The disclosed embodimentsare not limited to any specific type of processor configured in clientdevices 150.

Memory 510 may include one or more storage devices configured to storeinstructions used by processor 502 to perform functions related todisclosed embodiments. For example, memory 510 may be configured withone or more software instructions, such as programs 512 that may performoperations when executed by processor 502. The disclosed embodiments arenot limited to separate programs or computers configured to performdedicated tasks. For example, memory 510 may include a single program512 that performs the functions of the client devices 150, or program512 may include multiple programs. Memory 510 may also store data 516that is used by one or more programs 312 (FIG. 3 ).

Memory 510 may include instructions to perform image classificationand/or generate graphical user interfaces. For example, memory 510 maystore instructions that configure processor 502 to perform imageclassification operations, like extracting attributes from images,searching other images with similar attributes, and generating GUIs todisplay image classification results. Memory 510 may also includeinstructions to generate intermediate GUIs after receiving a searchquery and may include instructions to generate and modify the dynamicGUIs. For example, memory 510 may include instructions to generate GUIswith interactive icons and the functions or operations of theinteractive icons may be stored in memory 510.

In certain embodiments, memory 510 may store a vehicle identificationapplication 514 that may be executed by processor(s) 502 to perform oneor more image identification processes consistent with disclosedembodiments. In certain aspects, vehicle identification application 514,or another software component, may be configured to generate andtransmit request for servers to perform vehicle identification tasks.For example, vehicle identification application 514 may store CNNoperations and look-up tables that correlate identified attributes withavailable vehicles. Further vehicle identification application 514 mayinclude metafiles with information about vehicles, inventory status, andtheir related attributes. Moreover, vehicle identification application514 may be modifiable or updatable with patches received from otherelements of system 100. For example, when inventory search system 105issues a patch for a classification or identification model, vehicleidentification application 514 may run the patch to updateidentification models. In addition, vehicle identification application514 may perform operations of generating augmented reality icons tomodify video streams captured by client devices 150. Moreover, vehicleidentification application 514 may also include instructions to generateintermediary graphical user interfaces after performing preliminarysearches of an image, as further described in connection to FIGS. 16-17.

Vehicle identification application 514 may also configure processor(s)502 to communicate with inventory search system 105 or determine thelocation of client devices 150. For instance, through vehicleidentification application 514, client devices 150 may communicatefeedback of inaccurate results to inventory search system 105. Forexample, vehicle identification application 514 may generate HTTPrequests or other TCP/IP packets directed to inventory search system 105with user information. Alternatively, or additionally, vehicleidentification application 514 may generate queries for inventory searchsystem 105 based on attributes identified from an image, filters oroptions selected by a user, and locations of client devices. In suchembodiments, vehicle identification application 514 may also includeinstructions that configure processor(s) 502 to generate graphical userinterfaces based on responses or communications from other elements ofsystem 100. For example, vehicle identification application 514 mayinclude instructions to generate GUIs based on results obtained fromservers. Further, vehicle identification application 514 may storeclassification or identification models. For example, vehicleidentification application 514 may store the models generated by modelgenerator 120 (FIG. 3 ).

Additionally, vehicle identification application 514 may storeinstructions to compress or decompress images. In some embodiments,results images may be exchanged between client devices 150 and otherelements of system 100. For example, when transmitting feedback for aclassification model, client devices 150 may send an image with metadatato inventory search system 105. Further, when communicating results foran image classification, inventory search system 105 may send aplurality of images to client devices 150 including financinginformation and availability in inventories. When there are limitedbandwidths for network 170, like in cellular networks, images exchangesmay be expensive or slow. To improve the system vehicle identificationapplication 514 may perform compression and decompression of images toreduce the size of files being exchanged over the network.

For example, vehicle identification application 514 may configureprocessor(s) 502 to perform lossless image compression such asrun-length encoding, area image compression, DPCM and predictive coding,entropy encoding, adaptive dictionary algorithms (such as LZW), DEFLATE,or chain codes. Alternatively, or additionally, vehicle identificationapplication 514 may perform lossy compression/decompression methods suchas reducing the color space, chroma subsampling, transform coding,and/or fractal compression.

Further, in some embodiments, vehicle identification application 514 mayperform image compression specifically for vehicle images. For example,in some embodiments instead of transmitting or receiving the full imagethrough the network, vehicle identification application 514 may transmitor receive only key features of the image such as make, model, trim,and/or color. Based on attribute information, vehicle identificationapplication 514 may retrieve a sample image and make modifications togenerate images. For instance, inventory search system 105 identifiedvehicles of make Toyota™, from 2010, with different colors. Instead, ofsending multiple images of Toyotas from 2010 with multiple colors,inventory search system 105 may transmit the attributes information(i.e., make, model, and colors). Based on such information, vehicleidentification application 514 may retrieve a sample image of a Toyotafrom 2010 and make modifications to the sample image to generate imageswith the colors identified by inventory search system 105. For example,vehicle identification application 514 may generate a plurality ofthumbnails with the different colors. Such arrangement of sending onlyattributes may reduce network congestion.

In embodiments where vehicle identification application 514 may generatethumbnail images for graphical user interfaces in client devices 150,vehicle identification application 514 may store instructions forprocessor(s) 502 such as:

-   -   <src=“/folder_name/image_file_name.jpg?Action=thumbnail&Width=80&Height=80”/>    -   <src=“/folder_name/image_file_name.jpg?Action=thumbnail&Width=80&Height=80&algorithm-fill_proportional”/>    -   <src=“/folder_name/image_file_name.jpg?Action=thumbnail&Width=80&Height=80&Format=png”/>    -   <src=“{tag_myimage_value}?Action=thumbnail&Width=80&Height=80”/>    -   <img        src=“{tag_largeimage_path}?Action=thumbnail&Width=50&Height=50”/>        to generate thumbnail images with different characteristics        based on results from image recognition and/or information        received from inventory search system 105.

Moreover, vehicle identification application 514 may be configured tofeed an image to the model, for example to a model trained by modelbuilder 346 (FIG. 3 ). Then, vehicle identification application 514 mayreceive classification results with a confidence score for all theclasses it was trained. In some embodiments, the model may be trained torecognize 600 make/models and may normalize results, so the confidencevalues add up to 1.0. Vehicle identification application 514 may thenselect the class with the highest confidence score (or possibly the topfew) and show them to the user of client devices 150. As furtherdescribed below in connection with FIGS. 16-17 , a user may keep orchange that selection before conducting an inventory search. Further,vehicle identification application 514 may take in the selections offiltering options to narrow the inventory search.

I/O devices 504 may include one or more devices configured to allow datato be received and/or transmitted by client devices 150 and to allowclient devices 150 to communicate with other machines and devices, suchas other components of system 100. For example, VO devices 504 mayinclude a screen for displaying optical payment methods such as QuickResponse Codes (OR), or providing information to the user. I/O devices504 may also include components for NFC communication. 1/O devices 504may also include one or more digital and/or analog devices that allow auser to interact with client devices 150 such as a touch-sensitive area,buttons, or microphones. I/O devices 504 may also include one or moreaccelerometers to detect the orientation and inertia of client devices150. I/O devices 504 may also include other components known in the artfor interacting with inventory search system 105.

In some embodiments, client devices 150 may include a camera 520 that isconfigured to take still images or video and send it to other componentsof system 100 via, for example, network 170.

The components of client devices 150 may be implemented in hardware,software, or a combination of both hardware and software.

Referring now to FIG. 6 , there is shown a flowchart illustrating anexemplary process 600 for the generation of graphical user interfaces,consistent with disclosed embodiments. In some embodiments, process 600may be executed by inventory search system 105. For example, imagerecognizer 110 may perform steps in process 600, generate GUIs, andcommunicate them to client devices 150. In other embodiments, however,process 600 may be performed by client devices 150. For example,processor(s) 502 in client devices 150 may execute instructions invehicle identification application 514 (FIG. 5 ) to perform process 600and generate graphical user interfaces for displays in client devices150. Further, as previously discussed in connection to FIG. 1 , in someembodiments process 600 may be performed by multiple elements of system100. For example, some steps of process 600 may be performed byinventory search system 105, while other steps of process 600 may beperformed by client devices 150.

The description below of steps in process 600 illustrates an embodimentin which inventory search system 105 performs steps of process 600.However, as previously discussed other elements of system 100 may alsobe configurable to perform one or more of the steps in process 600. Forexample, client devices, and in particular processor(s) 502 may performone or more steps of process 600.

In step 602, inventory search system 105 may receive a captured image.The captured image may be captured by, for example, camera 520 in clientdevices 150. The captured image may be captured from a video stream orvideo feed. For example, the captured image may be an image that isbeing observed in a client device 150 running an augmented realityapplication, such as vehicle identification application 514. In someembodiments, the image may be an image of a vehicle, as shown in FIG. 15.

In some embodiments, the image received in step 602 may have a specificimage format and in step 602, inventory search system 105 may determinea file type of received image data. For example, inventory search system105 may identify that the received image is in JPEG format.Alternatively, inventory search system 105 may identify the image datais a raster format, such as GIF or TIFF. Also, inventory search system105 may be configured to determine the image data is in a vector formatsuch as CGM or XPS. In yet other embodiments, identification system maydetermine the image data is in a proprietary image format such as HEIC.

In step 604, inventory search system 105 may apply filters and/ornormalize image in preparation for extraction of parameters and imageclassification. For example, in step 604, inventory search system 105may convert the format of the received image data from the determinedfile type to a model file type. In some embodiments, converting thereceived image data to a new format may include executing a scriptingfunction, such as the following, where MODEL is the model format:

-   -   import image;    -   im=Image.open(‘test.jpg’);    -   im.save(‘test.MODEL’) #or ‘test.MODEL’;

Further, in step 604, inventory search system 105 may apply operationsto normalize the received image. For example, inventory search system105 may resize the received image data, blur, crop, despeckle, dither,draw on, flip, join, and re-sample based on the parameters of the modelimage.

Additionally. or alternatively, in some embodiments, in step 604,inventory search system 105 may normalize the image based onnormalization rules and determine a plurality of attributes from thenormalized image by extracting the attributes from the normalized imageusing a pretrained convolution neural network.

In step 606, inventory search system 105 may determine whether thefiltered and/or normalized image is acceptable for image classificationor identification. For example, after the normalization process thecontrast of the image may be poor. Because the transformation process ofstep 604 has degraded the quality of the image, the image data may notbe acceptable for identification or image classification. Thus, if thenormalized image data is not acceptable (step 606: No), inventory searchsystem 105 may generate an error GUI in step 606. For example, inventorysearch system 105 may generate an error GUI as further disclosed inconnection with FIG. 20 . Alternatively, in other embodiments, when anerror message is generated in step 606, inventory search system 105 maygenerate a computer image that is superimposed on client device 150notifying of the error with an augmented reality application.

However, if the filtered or normalized image data is acceptable foridentification (step 606: yes), inventory search system 105 may continueto step 610 and extract image attributes using classification oridentification models.

In step 610, inventory search system 105 may use a neural network modelor other machine teaming model to extract image parameters or attributesfrom the received image. For example, in step 610, inventory searchsystem 105 may import layers of a pre-trained convolutional neuralnetwork, determine features described in a target layer of thepre-trained network, and initialize a multi-class fitting model usingthe features in the target layer. In such embodiments, inventory searchsystem 105 may extract features of the captured image using aconvolutional neural network with max pooling layers, and mean, max, andL2 norm layers to compute extracted parameters. Additionally, inventorysearch system 105 may generate a file with the features it identifiedfrom the image. In some embodiments, in which the capture image is animage of a vehicle, the identified attributes or features may includevehicle make, vehicle model, and/or vehicle trim. Alternatively, oradditionally, in step 610 inventory search system 105 may get Histogramof Oriented Gradient (HOG) features for different images and feed thoseto a Logistic Regression or Support Vector Machine and train them toclassify the images and obtain preliminary results. Further, theidentified attributes or features may include vehicle make, vehiclemodel, vehicle body style, vehicle year, vehicle color, and/or vehicletrim.

Furthermore, inventory search system 105 may extract image attributesusing techniques as compiled functions that feed-forward data into anarchitecture to the layer of interest in the neural network. Forinstance, inventory search system 105 may implement the following scriptfor generating activations for a dense layer, determine imageparameters, and extract image attributes:

-   -   dense_layer=layers.get_output(net1.layers_[‘dense’],        deterministic=True);    -   output_layer=layers.get_output(net1.layers_[‘output’],        deterministic=True);    -   input_var=net1.layers_[‘input’].input_var;    -   f_output=t.function([input_var], output_layer);    -   f_dense=t.function([input_var], dense_layer).

In other embodiments, inventory search system 105 may implementengineered feature extraction methods in step 610. For example,inventory search system 105 may perform a scale-invariant featuretransform, Vector of Locally Aggregated Descriptors (VLAD) encoding, orextractHOGFeatures, among others to extract attributes from the image.

In step 612, inventory search system 105 may perform a preliminarysearch in a metafile using the extracted attributes as the searchparameters. The metafile may include a file format that stores multipletypes of data such as graphics, text, or vector file formats. Themetafile may provide support for an operating system's computergraphics. The metafile used or accessed in step 612 may include formatssuch as (WMF) Windows Metafile=(EMF) Enhanced Metafile, (EPS)Encapsulated PostScript, and (CGM) Computer Graphics Metafile. In someembodiments, the metafile may be stored in a local memory. For example,when process 600 is being executed by image recognizer 110, the metafileaccessed in step 612 for preliminary searches may be stored inrecognizer memory 220 (FIG. 2 ). However, in embodiments in whichprocess 600 is performed by client devices, the metafile may be storedIn memory 510 (FIG. 5 ). Alternatively, or additionally, metafile orlook-up tables for preliminary searches in step 612 may be stored inmemories of fast access or minimum latency. For example, metafile orlook-up tables for preliminary searches may be stored in local RAMmemories or in edge servers that can quickly respond to client devices150 queries.

In step 614, inventory search system 105 may rank preliminary resultsbased on a confidence level. Similar to keyword searches, imageclassification may also have results that are more relevant based ontheir relatedness with the sample image. In step 614, inventory searchsystem 105 may classify results from the preliminary search of step 612based on their relevance or confidence level. For example, models usedfor image classification or extraction may have different predictionaccuracies. In step 614, inventory search system 105 may incorporate aweighting for the preliminary results based on the identification orclassification model performance for the attributes extracted from theimage or the number of results that were obtained with the preliminarysearch. In some embodiments, the confidence level may be a percentage ofaccuracy or certainty. For example, the confidence level may be aprediction confidence percentage. When the prediction confidence for theimage classification is high (e.g., all attributes extracted from theimage are present in the result image and there are no overlappingclassification groups) the confidence level may be high and have arelated score of 90%. However, if the prediction confidence for theimage classification is low (e.g., only a few attributes were found inthe result image and there are other classification groups that alsoshare the same attributes) the confidence level may be low and have arelated score of only 10%.

In some embodiments, the confidence levels determine in step 614 may beconfigured as a probability function. In such embodiments, theconfidence levels of results from the search in step 612 may add to 100%(or 1), complying with definitions of probability function. However, inother embodiments, the confidence level may be unrelated fromprobabilities and may be determined with a weighted scored.

In step 616, inventory search system 105 may determine whether the imageclassification is limited by location preferences. For example, when theimage classification is being processed in client devices 150 or isreceived from a specific location, inventory search system 105 maytailor results from the image classification based on a location ofclient devices 150 and a range. For example, inventory search system 105may determine to focus results that are available within a 5-mile radiusfrom the location of client devices 150. Thus, in some embodiments,ranking of preliminary results may include determinations of distancewith respect to client devices 150. For example, inventory search system105 may determine a maximum distance for the plurality of first resultsbased on a preference stored in the one or more storage devices andeliminate a result from the first results associated with a locationoutside the maximum distance from the location of the client device.Further, in step 616 inventory search system 105 may cross-referencewith a list of available make/models in the area (or perform aninventory search in the background) and remove model prediction resultsabsent from that list. This may prevent the user getting an empty searchlater.

Further, inventory search system 105 may determine location preferencesbased on user records. For example, a user doing vehicle imageclassification may prefer certain dealers or may prefer certain areas tolimit the search. In such embodiments, inventory search system 105 maydetermine that there are location preferences (step 616: Yes) andcontinue to step 618 to eliminate preliminary results (from step 612)that are out of the location or the preferred range. This reduction ofresults may improve user experience by displaying only relevant resultsin screens that may have limited space in client devices 150. However,if there are no location preferences (step 616: No), for example theimage classification is not related to any location of client devices150 or the user has indicated that he does not want to restrict theimage classification, inventory search system 105 may skip step 618 andcontinue directly to step 620.

In step 620, inventory search system 105 may select a subset of thepreliminary results (from step 612) based on their confidence score andlevel and a threshold confidence or an aggregated confidence. Forexample, in step 620, inventory search system 105 may select a resultsubset with preliminary results that have a confidence level above 80%.Then, the subset of results would be limited to results with confidencelevel above 80%, having results that are highly relevant for the usersearch. Alternatively, or additionally, the subset of results may beselected based on combined confidence levels. In such embodiments,inventory search system 105 may add preliminary results in the subsetuntil achieving a target combined confidence level. For example, it thecombined confidence level is 90%, inventory search system 105 may addresults to the subset that have confidence levels of 50%, 20%, 10%, 5%,and 5%. While none of the individual results have a high confidencelevel, when combined, the five results have established a highconfidence that the image results are relevant for the user. Later, theuser may quickly review the subset of images to identify relevantresults. Generating the subset of results from the preliminary imageclassification is further discussed in connection with FIG. 13 .

In step 622, inventory search system 105 may determine if there arevisualization preferences that affect how the results should bepresented. The visualization preferences may include parameters for GUIsdisplayed in client devices 150. For example, the visualizationpreferences may include number of preliminary results that should bepresented, size of icons in GUI, type of interactions that the iconshave, whether classification results are presented with thumbnail imagesor only text results. Further, the visualization preferences may includepreferences of the format of the display. For example, the visualizationpreferences may specify whether results should be presented in a list,in a mosaic, or in a map. For example, the visualization preferences mayspecify that results should be presented in a list with thumbnails.Alternatively, or additionally, the visualization preferences mayspecific results should be presented in an image carousel.

If in step 622 inventory search system 105 determines there arevisualization preferences (step 622: Yes), inventory search system 105may truncate the subset of results selected in step 620 based on thevisualization preferences. For example, if the visualization preferencesindicate that only 5 results should be displayed to the user, inventorysearch system 105 may remove results to meet the visualizationpreferences. Alternatively, if the visualization preferences indicatethat more results should be displayed for the user (e.g., a user wantsto see all results available) in step 624 inventory search system 105may add additional results based on the visualization preferences.Further, in some embodiments, inventory search system 105 may defaultselect preliminary results based on the confidence level for defaultselecting results displayed in the graphical user interface. However, ifinventory search system 105 determines there are no visualizationpreferences (step 622: No) inventory search system 105 may skip step 624and continue to step 626.

In step 626, inventory search system 105 may determine whether aninteractive search filter should be displayed. The interactive searchfilter may display options for filtering image classification results.For example, the interactive filter may include colors, sizes, or shapesthat should be included or filtered out form the results of step 612.Inventory search system 105 may determine if the search filter should bedisplayed by retrieving user preferences for client devices to determineif a user has opted in viewing a filter for image classification. Inembodiments where process 600 is being performed by inventory searchsystem 105, inventory search system 105 may retrieve user informationfrom, for example, databases 180 (FIG. 1 ) to determine if aninteractive search filter should be displayed for an imageclassification. Alternatively, when client devices 150 perform process600, processor(s) 502 may retrieve user preferences or information formmemory 510—for example from vehicle identification application 514—todetermine if the interactive image classification filter should bedisplayed.

When determining an interactive search filter should not be displayed(step 626: No), inventory search system 105 may continue to step 628 andgenerate a GUI with results for the searched image. The GUI may includeicons for user selection or input. For example, in step 628, inventorysearch system 105 may generate thumbnails for classification results inthe subset to display a user the image classification results. Further,the GUI generated in step 628 may include information about the resultsor the search attributes. As further described in connection to FIG. 15, the GUI may include additional information about classificationresults such as price or locations.

In some embodiments, the GUI generated in step 628 may be tailored foraugmented reality applications. For example, when client devices 150 arerunning an augmented reality application and the captured image is animage from a video feed in the augmented reality application, the GUI ofstep 628 may be a modified image from the video feed that includesinformation about the classification results. For example, as furtherdiscussed in connection to FIG. 15 , in step 628 a video feed may bemodified to overlay icons that include information of the classificationresults or the subset of results. Alternatively, or additionally, theGUI generated in step 628 may include augmented reality GUI's thatdisplay information about the identified vehicle but also includebanners, lists, or carousel images, with classification results.

In some embodiments, the GUI in step 628 may display identifiedattributes of the image on the screen of client devices 150. The imagesmay be modified to display information such as price for the identifiedvehicle given the identified attributes. In other embodiments, however,inventory search system 105 may generate GUIs displaying cost andcondition information in online resources 140. For example, Inventorysearch system 105 may display information typically found for vehiclesin websites such as Kelly Blue Book® and dealer or automobilemanufacturer information. Further, in step 628 client devices maydisplay icons defined by thumbnail images of vehicles matching theattributes identified in step 610.

However, if in step 626 inventory search system 105 determines aninteractive search filter should be displayed (step 626: Yes), inventorysearch system 105 may continue to step 630. In step 630 instead ofgenerating graphical user interfaces for augmented reality or modifyingvideo feeds to include results information, inventory search system 105may determine available options of the subset of results. For example,when the image classification is for a vehicle classification, inventorysearch system 105 may determine which types of colors, trim, and/orfeatures are available for vehicles present in the subset with theselected preliminary results.

In step 632, inventory search system 105 may generate a search filtergraphical user interface that displays interactive icons for theavailable options determined in step 630. For example, if there aremultiple colors or trims from the vehicles in the subset of results,inventory search system 105 may generate a GUI with interactive iconsthat display different results and icons that the user may select tofilter out (or select in as preference) certain features for a secondsearch. The GUI with search filter is further disclosed in connection toFIG. 16 . In some embodiments, as shown in FIG. 16 , the options oricons displayed in the search filter GUI may include body style icons,color icons, and feature icons. Further, the icons may include a sliderselector for selecting a vehicle year. Moreover, in some embodimentsinteractive icons in the search filter GUI may be arranged based on theprobability score associated with corresponding first results.

In some embodiments, inventory search system 105 may continue top step634 and preselect certain filter icons in the GUI with search filter.The preselection may be based on confidence levels in the results in thesubset. For example, inventory search system 105 may determine apreselected group of options based on the attributes and preselectfilter icons in the first graphical user interface corresponding to thepreselected group of options. Moreover, inventory search system 105 mayperform operations so at least one of the filter icons is preselectedbased on the plurality of attributes and the at least one preselectedicon is displayed in a different color than other filter icons in thefirst graphical user interface.

Alternatively, or additionally, the preselection may be based on user,location, or visualization preferences. Further, as further discussed inconnection to FIG. 16 , in some embodiments preselected icons may have adifferent appearance. Moreover, in some embodiments, inventory searchsystem 105 or client devices may determine a group of available optionsbased on the first results and limit the display of filter or filtericons to only the group of available options for the selected firstresults.

Referring now to FIG. 7 , there is shown a flowchart of an exemplaryprocess for modifying graphical user interfaces, consistent withdisclosed embodiments. In some embodiments, as described below, process700 may be executed by inventory search system 105. For example, imagerecognizer 110 may perform steps in process 700, generate instructionsto modify GUIs, and communicate them to client devices 150. In otherembodiments, however, process 700 may be performed by client devices150. For example, processor(s) 502 in client devices 150 may executeinstructions in vehicle identification application 514 (FIG. 5 ) toperform process 700 and modify GUIs that are being displayed in clientdevices 150. Further, in some embodiments process 700 may be performedby multiple elements of system 100. For example, some steps of process700 may be performed by inventory search system 105, while other stepsof process 700 may be performed by client devices 150.

In step 702, inventory search system 105 (FIG. 1 ) may receive aselection of a preliminary result in client devices 150. For example,inventory search system 105 may receive an indication that a user ofclient devices 150 received a selection in the GUI that was generated inprocessed 600 (FIG. 6 ) and presented to a user in steps 632 or 628. Insuch embodiments, receiving a selection of preliminary result mayinclude receiving a selection of an interactive icon associated with apreliminary result (i.e., a result icon), which display a thumbnail forpreliminary results.

In step 704, inventory search system 105 may query a memory or adatabase to identify available options for the selected preliminaryresult. In some embodiments, inventory search system 105 may query localmemories to identify which options are available for the selectedpreliminary icon. For example, inventory search system 105 may present aGUI with 3 results icons after process 600. The result icons may includea BMW® and a Mercedes Benz®. From these preliminary results, inventorysearch system 105 may receive the selection of a BMW in step 702. Thus,in step 704 inventory search system 105 may determine options availablefor BMW. Inventory search system 105 may determine, for example, thatthere are BMW available in red, black, and white, and models 2018, 2019,and 2020. In some embodiments, inventory search system 105 may performthe query of step 704 with a local memory, to minimize latency andprovide a smooth user experience with minimum lag. For example, whenprocess 700 is performed by client devices 150, client devices 150 mayperform step 704 by querying memory 510 directly. In such embodiments,memory 510 may be updated periodically to keep up to date inventoriesand available options.

In step 706, inventory search system 105 may remove or add icons for theavailable options based on the identified available options in step 704.Continuing with the previous example of a BMW selection, because theavailable options are colors red, black, and white, and models 2018,2019, and 2020, in step 706 inventory search system 105 may remove iconsfor colors blue and green from GUIs presented to client devices 150.These other options are not available for the selected preliminaryresults and in order to improve user experience and reduce the number ofelements in the displays of client devices 150, which may be small,inventory search system 105 may dynamically remove icons associated withoptions that are unavailable for the selected preliminary results. Forexample, GUIs displayed in client devices 150 may include interactiveicons for options that allow users to narrow down image classificationresults. In step 706, inventory search system 105 may remove interactiveicons, such as buttons or toggle icons, associated with the unavailableoptions. For example, inventory search system 105 may perform operationsto remove icons or other attributes in the GUI such as:

  $( ″.Ivitem″ ).each(function() {  $(this).removeAttr('data-icon') $(this).data('icon','value-1');  icon = $(this).data('icon');});

Alternatively, or additionally, inventory search system 105 may addicons or elements based on the available options. For example, a usermay select both BMW and Mercedes Benz options. Then, the option ofcolors blue, green, and yellow may become available. In such scenarios,inventory search system 105 may dynamically update the GUI to add iconswith the available options that became available based on the selectionsof step 704.

In step 708, inventory search system 105 may receive a selection of oneor more options in the GUI. For example, inventory search system 105 mayreceive the selection of colors, models, conditions, via interactiveicons in the GUI.

In step 710, inventory search system 105 may query the memory ordatabase to identify other available options for the preliminary resultsthat overlap with the selected options of step 708. For example, when instep 702 a user selects BMW® and in step 708 a user selects color black,inventory search system 105 may query a memory or a database to identifywhat other available options are also available for black BMW®. Byquerying the memory, inventory search system 105 may identify that forblack BMW® the only available model is 2020 and new condition. In someembodiments, the memory queried in step 704 may be the same memoryqueried in step 710. Thus, local or rapid access memories may beaccessed in step 710 to dynamically update the GUIs based on userselections. For example, when process 700 is performed by processor 502,processor 502 may access memory 510 in step 710.

In step 712, like in step 708, inventory search system 105 may remove oradd icons for the available options based on the overlapping optionsidentified in step 710. For example, because for the selection of blackBMW the only model is 2020, inventory search system 105 may dynamicallyupdate the graphical user interface to eliminate interactive icons thatare no longer available based on the user selections. When adding icons,inventory search system 105 may retrieve images related for theavailable options. For example, inventory search system 105 may performoperations such as “piece1Labels[0].setIcon(newImageIcon(newimage.jpg”))” to add new icons in the GUI.

In step 714, inventory search system 105 may refresh or update therendering of the graphical user interface based on the selections andavailable options and displayed icons. For example, in step 714,inventory search system 105 may readjust size and location of icons inthe GUI to maximize the interaction with the user. To refresh GUIs,inventory search system 105 may update the group of available options bymapping the selected result icon with possible options in a look-uptable or metafile and modify the first graphical user interface byeliminating a filter icon associated with options not present in theupdated group of available options. Alternatively, or additionally,inventory search system 105 may update the group of available options bycorrelating the selected at least one of the plurality of filter oroption icons with other options in the look-up table and modify the GUIby eliminating filter icons associated with options not present in theupdated group of available options.

In step 716, inventory search system 105 may transmit the userselections to a server or other elements of system 100 to perform thefiltered search. For example, inventory search system 105 may transmitthe user selections to recognizer processor 230 to identify resultsrelevant for the user based on the user selections. Alternatively, whenprocess 700 is being performed by client devices 150, client devices 150may transmit the user selections to inventory search system 105 in step716 to receive classification results based on the user selections.

In some embodiments, in step 716, inventory search system 105 maytransmit information to online resources 140, which may have updatedinformation about dealer inventories and locations to provide accurateinformation for the user search. For example, inventory search system105 may send an information query to websites such as Kelly Blue Book tocollect condition information. In yet other embodiments, inventorysearch system 105 may send queries to dealer or automobile manufacturerwebsites to collect additional information of the vehicle.

Referring now to FIG. 8 , there is shown a flowchart of an exemplarysecondary search process 800, consistent with disclosed embodiments. Insome embodiments, as described below, process 800 may be executed byinventory search system 105. For example, image recognizer 110 mayperform steps in process 800 to identify inventory search results andcommunicate them to client devices 150. In other embodiments, however,process 800 may be performed by client devices 150. For example,processor(s) 502 in client devices 150 may execute instructions invehicle identification application 514 (FIG. 5 ) to perform process 800and search for secondary results. Further, in some embodiments process800 may be performed by multiple elements of system 100.

In step 802, inventory search system 105 may receive the selection ofone or more interactive icons in a GUI. For example, inventory searchsystem 105 may receive the selection of one or more icons in the GUIgenerated via process 600 (FIG. 6 ). The selection may include one ormore preliminary results and one or more options. As previously,discussed, in connection with FIG. 7 , the selection may dynamicallychange the appearance of the GUI and eliminate or add interactive icons.

In step 804, inventory search system 105 may generate a query includinguser selections in the GUI. For example, when a user selects the resulticon of a BMW and options for model ‘2010,’ color ‘black,’ and condition‘new,’ inventory search system 105 may generate a query for a serverthat includes these parameters for a search. For example, inventorysearch system 105 may generate and HTTP query for a server in system 100or for databases 180. Alternatively, or additionally, the query of step804 may be coded as a URL or URI that may be decoded by other elementsof system 100. In some embodiments, the query may include additionalinformation from the one selected by the user. For example, in someembodiments the query may include location information of clientdevices. Additionally, the query may include user preferences. Forexample, in addition to the user selections of vehicle make, color, andmodel, the query of step 804 may include location coordinates of clientdevices 150 associated with the query and user preferences, such as alimited listed of preferred dealers. In embodiments where client devices150 implement process 800, client devices 150 may generate and transmitthe query to other elements of system 100. For example, client devices150 may generate a search query for inventory search system 105.Alternatively, or additionally, client devices 150 may generate a searchquery for inventory search system 105 including the vehicle make, thevehicle model, an option selected in the filter icons, a client devicelocation, and user account information associated with the clientdevice.

In step 806, inventory search system 105 may receive an inventory searchresults with available items that match user selections and/or otherparameters transmitted in the query of step 804. For example, based onthe query of step 804 databases 180 (or other element of system 100 withinventory information) may provide a list of results that match theattributes selected by the user and/or included automatically in therequest. Continuing the previous example with a query including BMW andoptions for model ‘2010,’ color ‘black,’ and condition ‘new,’ in step806 inventory search system 105 may receive a list of vehicles availablein the inventory that match the user selections and/or parameters in thequery. The inventory search results of step 806 may include thumbnailimages of the results and be associated with information specifyinginformation such as location or price.

In step 808, inventory search system 105 may receive information fromfinancing services availability for results in the inventory search. Asfurther described in connection with FIG. 14 , inventory search system105 may send the inventory results of step 806 and client information toa financing service. The financing service may then determine if thereis financing availability for the client and the particular vehicle. Forexample, based on the client information, price of the vehicle, andlocation of the vehicle (e.g., the specific automobile dealer offeringthe vehicle), a financing service may determine if it can providefinancing to the client if the client decides to buy the vehicle. Insome embodiments, the financing availability determinations may be doneindependently for each one of inventory search results of step 806.However, in other embodiments, the financing availability may bedetermined for groups of inventory results or for groups of clientdevices 150. Thus, in step 808, inventory search system 105 may receivefinancing services availability for the inventory results and in step810 inventory search system 105 may march inventory results with theircorresponding financing availability. For example, in step 810,inventory search system 105 may generate an array associating inventoryresults with financing options or conditions. In some embodiments,inventory search system 105 may perform matching functions in step 810such as:

  match <- function(dat1, dat2, dat3){  selectedRows <- (dat1$ID %in%dat2$ID & dat1$ID %in% dat3$ID)  matchdata <- dat1[selectedRows] return(matchdata)) prepost.match.data = match(all.data, pre data,post.data)

In step 812, inventory search system 105 may rank the inventory resultsin step 806 (which may also be secondary results because they may bebased on primary results in process 600) based on relevance, popularity,financing availability, and/or distance from location. In someembodiments, the ranking may be based on user preferences or selections.For example, if the user prefers ranking based on popularity, theninventory search system 105 may rank secondary results based onpopularity. In other embodiments, however, inventory search system 105may rank inventory of secondary results by considering the multiplevariables together.

In step 814, inventory search system 105 may generate a GUI displayingthe inventory search results. In embodiments in which there is financingavailability for one or more of the inventory search results, the GUI instep 814 may include the financing availability. For example, the GUImay display secondary results with financing availability. In suchembodiments, each one of the secondary results may be presented as aninteractive icon that may link to finance service systems. An exemplaryGUI generated in step 814 is further described in connection with FIG.18 .

Referring now to FIG. 9 , there is shown a flowchart of an exemplaryprocess for repopulating primary results, consistent with disclosedembodiments. In some embodiments, as described below, process 900 may beexecuted by inventory search system 105. For example, image recognizer110 may perform steps in process 900, generating or modifying GUIs todisplay additional results and communicate them to client devices 150.In other embodiments, however, process 900 may be performed by clientdevices 150. For example, processor(s) 502 in client devices 150 modifyGUIs presented in a display based on process 900 and user selections.Further, in some embodiments process 900 may be performed by multipleelements of system 100.

In step 902, inventory search system 105 may receive a selection of arepopulation icon. As further described in connection with FIG. 16 , theGUI generated in process 600 (FIG. 6 ) may include a repopulation icon.A user may select the repopulation icon when it is not satisfied withthe preliminary results presented or when it wants to review additionaloptions. Then, process 900 may get triggered by the user selection of arepopulation icon.

In step 904, inventory search system 105 may transmit to a server ordatabase a notice of the user request for repopulating the results. Insome embodiments, user requests for additional preliminary results mayindicate the preliminary results were not relevant or inaccurate. Inturn, this lack of accuracy may require predictive model corrections.For example, that a user indicates a result was not relevant mayindicate model generator 120 is issuing inaccurate models. Thus, in someembodiments a message may be transmitted to model generator 120 togenerate a corrected model. For example, as further described inconnection to FIG. 12 , user inputs when selecting the repopulating iconin step 902 may be used to add data to a training dataset that is thenused to update classification or identification models.

In step 910, inventory search system 105 may eliminate results thatmatch the previously presented results. For example, if previouslypresented results included BMW and Mercedes Benz and the user selectedto repopulate, inventory search system 105 may eliminate any resultsthat have BMW and Mercedes Benz in step 910. In this way, inventorysearch system 105 assures that the user will see new results whenselecting the repopulate icon. For example, inventory search system 105may eliminate previously presented results from the subset of resultsthat is used to generate the results icons. Further, in eliminatingresults that match previously presented results, inventory search system105 may execute instructions to substitute images in the GUI such as:

  img = imagei if (label != null) {   label.seticon(new ImageIcon(img);  label.repaintO;}  super.paintComponent(g); drawImage(img.getimage(),0, 0, getWidth(), getHeight(), this);

As discussed below in connection with FIG. 17 , these repopulation ofpreliminary results in the GUI may also generate alerts for theidentification model signaling improper results.

In step 912, inventory search system 105 may determine if there arestill results available in a group of results to present, despite theelimination of results in step 910. For example, inventory search system105 may determine if the subset of preliminary results to be presentedis void. If there are results available to display (step 912: Yes),inventory search system 105 may continue to step 914 and modify the GUIto display the results not eliminated in step 910. This will display newresults that match the attributes in the sample image but have not beendisplayed for the user. However, if there are no results available todisplay (step 912: No), inventory search system 105 may conclude thatthere are not alternative results to show to the user in response to therepopulate request and continue to step 916. In step 916, inventorysearch system 105 may generate a GUI requesting a new picture to attempta new image classification. For example, inventory search system 105 maygenerate a GUI similar to the one presented in connection to FIG. 20 toretake an image and attempt a new image classification. Alternatively,in step 916 a GUI may request a user reopen the augmented realityapplication, such as vehicle identification application 514, to capturea new sample image.

In step 918, inventory search system 105 may repeat the preliminarysearch using a newly captured image. That is, inventory search system105 may repeat process 600 for the new image, extracting attributes andidentifying similar images in local memories to present preliminaryresults. However, if the results obtained in step 918 match the resultspreviously presented, when receiving a selection of the repopulationicon in step 912, inventory search system 105 may continue to step 920and generate a graphical user interface. In such embodiments, therepeated selection of a repopulation icon may indicate that theidentification model is underperforming for the specific attributes thatwere identified. In such embodiments, inventory search system 105 maysend a notice to model generator 120 to note the deficiencies of themodel and start taking corrective measurements. Alternatively, in step920 inventory search system 105 may be configured to display a fullinventory of secondary results when the preliminary search of step 918is unsuccessful as providing the same inaccurate results.

Process 900 has been described as being performed by inventory searchsystem 105. However, as previously discussed, in some embodiments clientdevices 150 may be performing process 900 and perform the determinationsof process 900 and attribute extractions with processor 502.

Referring now to FIG. 10 , there is shown a flowchart of an exemplary aclient query resolution process 1000, consistent with disclosedembodiments. In some embodiments, process 1000 may be executed byinventory search system 105. In other embodiments, however, process 1000may be performed by online resources 140. For example, online resources140 related to the determination of financing availability may performprocess 1000. Further, in some embodiments process 1000 may be performedby multiple elements of system 100.

In step 1002, inventory search system 105 may receive a query fromclient devices 150. For example, inventory search system 105 may receivethe query described in step 804 from client devices 150. The query mayinclude search parameters (such as user selections in the interactiveicons), an account information based on the user of the client devices150, and/or client identification in the vehicle identificationapplication 514.

In step 1004, inventory search system 105 may receive information aboutthe availability of vehicles. For example, when the image classificationis for vehicle images, inventory search system 105 may receive inventoryinformation from online resources 140 associated with vehicle dealers,vehicle manufacturers, and/or aggregator websites. Alternatively, oradditionally, databases 180 may send inventory information to inventorysearch system 105 with items available. In some embodiments, theinventory search may be limited to some parameters of the search query.For example, the list of available items received in step 1004 may belimited to items within a location or limited to certain userpreferences.

In step 1006, inventory search system 105 may determine availability ofitems in the inventory that match elements of the query. In someembodiments, inventory search system 105 may generate an array, orsimilar information structures, correlating the query elements withitems in the inventory received in step 1004. For example, inventorysearch system 105 may determine which items from the inventory listmatch query parameters.

In step 1008, inventory search system 105 may receive information offinance availability based on an account associated with the query andthe items in the inventory list that match the query requirements. Insome embodiments, the finance availability information may be receivedfrom online resources 140. For example, as further described inconnection with FIG. 14 an online resource 140 associated with a financeservice may provide financing availability for items in the inventorylist.

In step 1010, inventory search system 105 may calculate financingconditions based on the financing availability and the items in theinventory. For example, inventory search system 105 may determinemonthly payments, interest rates, and/or other financing considerationsin step 1010 for elements in the inventory list.

In step 1012, inventory search system 105 may determine availablevehicles for the query and associate financing conditions. For example,inventory search system 105 may generate a composite list that includesboth the vehicles from the inventory that match the query attributes orrequirements with the financing conditions calculated in step 1010.

In step 1014, inventory search system 105 may transmit inventory resultsand financing information to other elements of system 100. For example,inventory search system 105 may transmit a list of vehicles that matchthe query requirements with associated financing conditions to clientdevices 150. In some embodiments, the inventory results and financinginformation may be transmitted with graphical elements, such as imagesor interactive icons, to generate GUIs in client devices 150. Forexample, information transmitted in step 1014 may be used to generateGUIs as further discussed in FIG. 18 .

Referring now to FIG. 11 , there is shown a flow chart of an exemplaryillustrating a process 1100 for generating an identification model,consistent with disclosed embodiments. Process 1100 may be performed byinventory search system 105. For example, process 1100 may be executedby model builder 346 (FIG. 2 ) and may be configured to generateclassification models, such as convolutional neural networks, toidentify images. In other embodiments, however, process 1100 may beperformed by client devices 150. For example, process 1100 may beperformed by processor 502.

In step 1102, inventory search system 105 may partition images into atraining data set and a validation data set. For example, inventorysearch system 105 may receive data representing a plurality of images ofvehicles. The images may be associated with metadata describingattributes of the vehicle in the image. Inventory search system 105 maydivide the images of the vehicles and generate two groups, one to trainthe convolutional neural network and a second to validate the model.Alternatively, or additionally, inventory search system 105 maysegregate images to train and validate recurrent neural network orrandom forest for image identification.

In step 1104, inventory search system 105 may generate an input arraybased on features of the training data set. For example, inventorysearch system 105 may generate a variable including feature informationof images in the training data set.

In step 1106, inventory search system 105 may generate output vectorsbased on metadata of the training data set. For example, based on theimages in the training data set, the identification system may generatea desired output vector identifying vehicle make and model that isincluded in the training data set.

In step 1108, inventory search system 105 may determine hyperparametersand activation functions to initialize the model to be created. Forexample, inventory search system 105 may select a number of layers andnodes, and determine whether the network will be fully or partiallyconnected. In addition, in step 1108 inventory search system 105 maydetermine the dimensionality of the network and/or determine stacks ofreceptive field convolution networks. Alternatively, or additionally,inventory search system 105 may determine a pixel input resize value. Insome embodiments, the selection of the pixel input resize value may bedetermined by a neural net architecture and the selection of the neuralnet architecture may be based on a required identification speed.

Moreover, in step 1108 inventory search system 105 may also associatethe model with one or more activation functions. For example, inventorysearch system 105 may associate the model with one or more sigmoidalfunctions. Moreover. In step 1110 inventory search system 105 mayinitialize weights for synapsis in the network.

In step 1112, inventory search system 105 may apply the input arraybased on features of training data set of step 1104 to calculate anestimated output in step 1114 and a cost function. In step 1120,inventory search system 105 may determine whether the cost function isbelow a threshold of required accuracy, which may be specified by theuser. If inventory search system 105 determines that the cost functionis not below a threshold and the required accuracy has not beingachieved (step 1120: No), inventory search system 105 may continue tostep 1118 and determine a gradient to modify weights in synapsis ormodify the activation functions in the different nodes. However, if thecost function if below a threshold (step 1120: Yes), identificationsystem may accept the model and communicate the model to a server insystem 100 and/or client devices 150 in step 1122.

Referring now to FIG. 12 , there is shown a flow chart of an exemplarypatch generation process 1200, consistent with disclosed embodiments. Insome embodiments, process 1200 may be executed by inventory searchsystem 105. For example, model generator 120 may perform steps inprocess 1200, generating patches for identification or classificationmodels. The patches may then be transmitted to client devices 150 toupdate image classification or identification models or improve imageclassification accuracy. In other embodiments, however, process 1200 maybe performed by other elements of system 100. For example, onlineresources 140 or computing clusters 160 may perform process 1200.Further, in some embodiments process 1200 may be performed by multipleelements of system 100.

After a model is trained and validated, the model may return inaccurateresults for certain scenarios. For example, images that are differentfrom the ones used in the training or validation data sets in process1100 may return results that are inaccurate or irrelevant. Further,accuracy metrics of the identification models may not generalize forother kind of image. For example, some classification or identificationmodels may be tailored for the training datasets. Thus, it may bedifficult to have image classification or identification models that arehighly accurate for multiple sample images. Also, because imagerecognition and identification models may not provide clear top results(i.e., multiple results may match attributes extracted from a sampleimage), it may be difficult to identify results that should be presentedto the user. Particularly in portable and small screens, where isimpractical to show multiple classification results, the plurality ofresults that may result from classification models may make it difficultto identify the selected results.

The disclosed systems and methods address these technical difficultieswith GUIs that allow users to easily interact with model generators toprovide feedback and correct modeling parameters. For example, throughprocess 1200 inventory search system 105 may identify scenarios in whichthe model is not performing well and update the model to correctmistakes.

Process 1200 may begin with step 1202. In step 1202, inventory searchsystem 105 may receive a captured image along with user input. Asfurther described in connection with FIG. 17 , in some embodiments auser may be presented a GUI that allows the user to input information ofthe scanned image. In such embodiments, the user may notify inventorysearch system 105 that the preliminary results presented in the GUI areincorrect and specify parameters that the model should have identified.The image received in step 1202 may be the image that resulted in theinaccurate results and may be labeled or tagged with the informationthat the user inputted as the correct information. For example, a usermay have captured an image that the model identified as a black BMW.However, the user disagrees with the presented result because theresults should show a black Mercedes Benz. The user can, thus, notifythat the image classification was inaccurate and specify that correctresult. In this example, inventory search system 105 may receive thecaptured image that resulted in the inaccurate result that has beenmodified to include metadata of the user input in step 1202. In suchembodiments, client devices 150 may perform operations to compressand/or modify the metadata of the image when sending it as feedback toinventory search system 105. For example, client devices 150 may modifyEXIF metadata and perform operations such as ‘$ exiftool“-MakeMetadata-=Mercedes”’ to change the metadata of images using theuser's input.

In step 1204 inventory search system 105 may determine characteristicsof the captured image that was received in step 1202. For example,inventory search system 105 may perform attribute extraction usingpre-trained networks to identify characteristics or attributes in theimage. In some embodiments, in step 1204 inventory search system 105 mayre-analyze the captured image to determine attributes such as make,model, trim, and/or color. These extracted values may then be comparedwith the user input received in step 1202 and inventory search system105 may identify inconsistencies. The inconsistencies may then specifywhich parameters the identification model is missing and identifyelements in which the model is underperforming.

In step 1206, inventory search system 105 may determine whether theidentified image characteristics have received similar user input. Toidentify repeated mistakes, inventory search system 105 may determine ifsimilar images (i.e., images with similar attributes as identified instep 1204) are receiving similar user input. For example, inventorysearch system 105 may determine whether images that the model believesto be black BMW are being frequently corrected for black Mercedes Benz.Alternatively, inventory search system 105 may determine whether imagesthat the model believes to be BMW 3 series (i.e., make/model) have beenfrequently corrected for a different make model combination, such as BMW5 series. Repeated corrections of similar images may indicate that themodel is biased, not property trained, or has a consistent issue withspecific attributes of black BMW. If inventory search system 105determines that identified image characteristics have received similaruser input (step 1206: Yes), inventory search system 105 may continue tostep 1208 and create an exception for identified characteristics. Forexample, if the model is frequently outputting BMW but users frequentlycorrect the results for Mercedes Benz, inventory search system 105 maycreate a model exception. The exception may specify that theclassification model should output Mercedes Benz when identifying BMW.Including these exceptions in the identification model may be performedwith conditional routines that substitute results based on theexception.

In step 1210, inventory search system 105 may create a patch foridentification or classification models to include the exception. Thepatch may be a script that modifies the model response to images withcharacteristics similar to the ones identified in step 1204. Forexample, the patch may address the inconsistent or inaccurate resultsand introduce functionality regressions. The patch developed in step1210 may be packaged in a form that is easily deployable and installableby users. For example, the patch may be configured to automaticallyexecute commands in client devices 150 and/or invoke patch managementsystems in the Operating System (OS) of client devices 150. Thepackaging format of the patch may depend on the installation technologyused for the software. For example, packaging formats of the patch mayinclude Windows Installer for Windows and RPM (Red Hat PackageManagement) for Linux or iOS. Further the patch may be an executablefile such as an .exe.

The patch with exception of step 1210 may be configured to be in aself-contained package that is identified by name, target application oroperating system, processor architecture, and language locale. Inaddition, the patch's relationship with previously released patches mayalso be described within the patch file. Further, inventory searchsystem 105 may be configured to support silent installation through thecommand line of the target operating system. The silent installationoptions may be the same for multiple patches created by inventory searchsystem 105 to facilitate administrative cost of incorporating the patchinto patch management solutions. Moreover, the patch may includeoperations for services/daemons that can be restarted automatically, andin-memory code can be patched while running. Moreover, the patch createdin step 1210 may be protected from tampering and their integrity may beverifiable through digital signatures, hashes, or checksums. In someembodiments, digital signatures may be preferred.

In step 1206 inventory search system 105 may determine whethercharacteristics identified from the image have not previously receivedsimilar user input. That is, users have not provided consistent feedbackfor the determined characteristics. For example, inventory search system105 may determine the inaccuracy identified by the user and reported instep 1202 has not been received before. This may indicate that the modelhad only one inaccurate result, which is not frequent, or that differentusers have been providing varying feedback for similar images—indicatingthe model may not be consistently wrong, but only in certain occasions.Then, if inventory search system 105 determines the identifiedcharacteristic has not received similar user input (step 1206: No),inventory search system 105 may skip steps 1208 and 1210 and continue tostep 1212.

In step 1212, inventory search system 105 may label the captured imagewith the user input as metadata. For example, in embodiments in whichclient devices do not modify images received in step 1202, but insteadsend unprocessed and separated captured images and user input, inventorysearch system 105 may label the captured image with inaccurate resultsusing the user input. For example, inventory search system 105 maymodify images by including metadata using operations such as:

  var jpeg = new JpegMetadataAdapter(pathToJpeg); jpeg.Metadata.Comment= ″labels″; jpeg.Metadata. Title = ″labeltitle″: jpeg.Save();jpeg.SaveAs(Path);

Alternatively, or additionally, inventory search system 105 may modifythe captured image by modifying metadata related to the captured imageto include user input in feedback GUIs, as further described inconnection to FIG. 17 .

In step 1214, inventory search system 105 may include the capturedimage, now labeled using the user input, in the model training and/orvalidation datasets. With the disclosed system of GUIs that allowproviding feedback to model generator 120, user interactions in clientdevices 150 may be leveraged to seamlessly increase the images in thetraining dataset using user-labeled images to enhance the accuracy ofthe models.

In step 1216, inventory search system 105 may retrain the model used forclassification or identification of images. For example, inventorysearch system 105 may retrain convolution layers used in a CNN for imageidentification. Further, in step 1218 inventory search system 105 mayretrain connection layers of the model. For example, in steps 1216-1218inventory search system 105 may take a pretrained network and remove thelast fully connected layer, then treat the rest of the net as a fixedfeature extractor for the new dataset. In such embodiments, inventorysearch system 105 may recompute vectors for every image that containsthe activations of the hidden layer immediately before the classifier.For example, inventory search system 105 may execute updates to CNNlayers, which may include ReLUd (i.e. threshold at zero), to retrainconvolution and connection layers in steps 1216-1218.

In step 1220, inventory search system 105 may determine updated modelaccuracy. For example, inventory search system 105 may perform costfunction evaluations of the retrained model, as further described inconnection with FIG. 11 .

If the model is acceptable, inventory search system 105 may create apatch for an existing model and/or an updated classification oridentification model in step 1222. For example, inventory search system105 may create a patch that updates a model running in client devices150. In such embodiments, inventory search system 105 may use APIs toaccess offline models and update the parameters of the model based onthe recalculations in steps 1216-1218. Further, in step 1222 inventorysearch system 105 may access an application in client devices 150, suchas vehicle identification application 514 to update the model.Additionally, or alternatively, inventory search system 105 maycustomize image classification models. For example, inventory searchsystem 105 may build a custom TensorFlow Lite model to run locally onthe end user's device or a patch to customize the TensorFlow. Further,the patch may include a classification model exception for attributesidentified in step 1204, and include exceptions based on user input.

In step 1224, inventory search system 105 may deploy the patch and/orupdated model the client devices 150. For example, once the patch hasbeen verified to address the issues identified in step 1206 and thepatch passes compatibility testing, inventory search system 105 maynotify client devices 150 of the availability of the patch so they candownload and deploy it on their systems. In some embodiments, inventorysearch system 105 may provide information on the severity of theproblem, the urgency of the patch, and potential mitigations.

In some embodiments, inventory search system 105 may employ patchdistribution systems/services, such as online resources 140, thatperform an automated method for easily distributing patches to clientdevices 150. This patch distribution service may include predeterminedbandwidth availability to handle the often high number of concurrentusers attempting to download the patch. In some embodiments, inventorysearch system 105 may consider not only the bandwidth of theirdistribution system as a whole, but also the specific bandwidth requiredto download an individual patch. To prevent network congestion andservice unviability, inventory search system 105 may limit the number oftransmissions of patch and/or updated models in step 1224. Particularly,if the patch or updated model includes several megabytes, may requiresome coordination of patch deployment. In some embodiments, transmittingthe patch in step 1224 may include transmitting credentials to clientdevices 150 before the download. To prevent viruses to be disguised aspatches, inventory search system 105 may provide certificates tominimize risk. The patched or updated model transmitted in step 1224 mayminimize errors associated with inaccurate identification of images withthe characteristics identified in step 1204.

Referring now to FIG. 13 , there is shown a flowchart of an exemplaryprocess for selecting results displayed in a graphical user interface,consistent with disclosed embodiments. In some embodiments, process 1300may be executed by inventory search system 105. For example, imagerecognizer 110 may perform steps in process 1300, generating ormodifying GUIs to display results and communicate them to client devices150. In other embodiments, however, process 1300 may be performed byclient devices 150. For example, processor(s) 502 in client devices 150modify GUIs presented in a display based on process 1300 and userselections. Further, in some embodiments process 1300 may be performedby multiple elements of system 100.

In step 1302, inventory search system 105 may identify classificationresults that match search parameters in the sample image captured by auser. For example, in step 1302 inventory search system 105 may identifyresults based on images received from client devices 150 using searchprocesses further described in connection with FIG. 6 . In someembodiments, results identified in step 1302 may be associated with alevel of confidence or an estimated percentage accuracy. For example,each one of the results identified in step 1302 may be associated with apercentage of probability in a probability density function, such asprobability of 50% or 30%.

In step 1304, inventory search system 105 may generate a group of modelor classification results based on corresponding confidence levels. Insome embodiments, the selected results may be based on a achieving athreshold confidence level. For example, after the image identificationprocess inventory search system 105 may have 10 results. Each result maybe associated with a confidence level. For example, the results may haveconfidence levels of 50%, 15%, 10%, 5%, 4%, 4%, 4%, 4%, 2%, 2%. Then,inventory search system 105 may select results that reach a confidencethreshold of 80%, selecting the results with 50%, 15%, 10%, 5%. Thisselection of classification results based on confidence levels mayenable inventory search system 105 to more efficiently use the limitedscreen in client devices 150. By reducing the number of results that arepresented to the client, while achieving a minimum threshold ofconfidence, inventory search system 105 may improve the user experience,minimize network congestion, but have a high likelihood the user willsee relevant results.

In step 1306, inventory search system 105 may determine whether thenumber of results in the group generated in step 1304 is below a minimumnumber. Continuing with the previous example, in step 1304 inventorysearch system 105 may have created a group of results with 4 items,respectively with confidence levels of 50%, 15%, 10%, 5%. However,inventory search system 105 may be configured to show a minimum of 5results in a GUI. This may be a user visualization preference.Alternatively, or additionally, the minimum may be determined on thetype of client devices 150 that will display the GUI with results. Forexample, the minimum number of results for the group may be greater forclient devices 150 with large screens (such as tablets) than for clientdevices 150 with small screens (such as smartwatches). As previouslydiscussed in connection to FIG. 7 , queries from client devices 150 mayinclude device information including the type of screen available in thedevice. Further, in embodiments in which client devices 150 performprocess 1300, client devices 150 may determine the minimum number ofelements in the group based on user configurations, concurrentapplications, and the characteristics of the display.

If inventory search system 105 determines that the number of results inthe group is below the minimum (step 1306: Yes), inventory search system105 may continue to step 1308 and determine additional results toinclude based on attributes and/or advertising preferences. For example,in step 1308 inventory search system 105 may identify one or more of theadditional elements that were identified in step 1302, selecting one ofthe results associated with 4% confidence value to meet the minimumnumber. Alternatively, or additionally, the selection of the additionalresults to add to the group generated in step 1304 may be based onadvertising considerations. For example, inventory search system 105 mayprefer certain vehicle dealers based on advertising considerations.Items related to the preferred dealers may be added to the group in step1308. In step 1310, the additional results identified in step 1308 maybe added to the group until achieving the minimum number of results tobe display. In some embodiments steps 1308-1310 may be iterative, addingresults to the group one by one and making independent determinationsuntil reaching the minimum number. Alternatively, or additionally,inventory search system 105 may be configured to perform a sequence ofoperations of determining whether a number of the results in the group(or subset) of step 1304 is below a minimum number, determining alocation of client devices 150 when the number of the first results inthe subset (or group) is below the minimum number, selecting a group ofthe first results based on the location (where results in the groupbeing different from results in the subset), and adding the group offirst results to the subset in step 1310.

However, if in step 1306 inventory search system 105 determines that thenumber of results is not below a minimum (step 1306: No), process 1300may continue directly to step 1312. In step 1312, inventory searchsystem 105 may determine if the number of results in the group is abovea maximum. Similarly, to the minimum number of results, the maximumnumber of results may be based on visualization preferences or displaysize. For example, when client devices 150 include smartwatches, thedisplay may be limited to a single result. Then, the maximum number maybe one. Alternatively, when client devices 150 include smartphones, thedisplay may be limited to three classification results.

When in step 1312 inventory search system 105 determines that the numberof results is above a maximum number (step 1312: Yes), inventory searchsystem 105 may continue to step 1314 and truncate results. In someembodiments, inventory search system 105 may truncate results based onconfidence levels and/or advertising preferences. For example, from thegroup of results with confidence levels 50%, 15%, 10%, 5% (selected instep 1304), inventory search system 105 may eliminate the 5% results,because it has the lowest confidence level. However, in otherembodiments inventory search system 105 may truncate elements based onadvertising preferences. For example, inventory search system 105 mayeliminate the 10% confidence result when the 5% confidence result isassociated with a preferred dealer. Thus, inventory search system 105may determine whether the number of first results in the subset is abovea maximum number in step 1312, the maximum number being based onvisualization preferences, to then eliminate first results in the subsetassociated with lower probability scores until the number of firstresults in the subset matches the maximum number in step 1314.

Alternatively, or additionally, inventory search system 105 may retrievefrom a local memory a set of visualization preferences, the set ofvisualization preferences including a maximum number of results to bedisplayed in the first graphical user interface. Then, inventory searchsystem 105 may eliminate a first result based on a correspondingconfidence score to match the maximum number. In such embodiments,visualization preferences may include a list of preferred filter iconsand only preferred filter icons are displayed in the first graphicaluser interface.

However, if inventory search system 105 determines that the number ofresults is not above a maximum number (step 1312: No), inventory searchsystem 105 may continue to step 1316 directly. In step 1316, inventorysearch system 105 may determine if the confidence level of top resultsin the group, after additional or eliminating results in steps1308-1312, are within a confidence level range. For example, inventorysearch system 105 may evaluate whether results are within a 10%confidence level. This may be associated with multiple results that allhave similar probabilities of being relevant for the user. For instance,if the group of results generated in step 1304 have confidence levels of30%, 25%, and 22%, then inventory search system 105 may determine thatthe top results in the group have confidence levels within theconfidence level range. However, if the results in the group haveconfidence levels of 50%, 15%, 10%, then inventory search system 105 maydetermine that the confidence levels of classification results are notwithin the range.

If inventory search system 105 determines that the confidence levels arewithin a range (step 1316: Yes), process 1300 may continue to step 1318and pre-select or default multiselect top results within the confidencelevel range and generate a GUI with pre-selected results. For example,based on the model output, inventory search system 105 may determine howmany results should be displayed in a GUI. Further, inventory searchsystem 105 may determine whether to auto-select the top prediction ormultiselect (e.g., based on a threshold) top results having similarconfidences scores. For instance, inventory search system 105 maydetermine how many of the results should be displayed and that allresults with a confidence about 40% should be displayed and defaultmultiselected. (e.g., default multiselect results with confidence of45%, 44%). In some embodiments, the pre-selection or default selectionmay be based the specific output of classification models. For instance,if the model only predicts make/model, inventory search system 105 mayonly default select with make/model. However, if the predictive model istrained for make/model/Year/trim, body style, and color, inventorysearch system 105 could pre-select for the different parameters.

By pre-selecting or default multiselecting results, inventory searchsystem 105 may improve the user experience by automating some of theuser selections. For example, pre-selecting results based on confidencelevel in GUIs like the ones presented in FIGS. 16-17 , may facilitateuser interaction and improve maneuverability of the GUI preventingpotential inaccurate selections. For example, inventory search system105 may identify top results in the subset (the top results beingassociated with highest probability scores), determine whetherdifferences of corresponding probability scores between top results arewithin a range and pre-select interactive icons correspond to the topresults when the differences of corresponding probability scores betweentop results are within the range.

However, if inventory search system 105 determines that the confidencelevels of results are not within the range (step 1316: No), process 1300may continue to step 1320. In step 1320, inventory search system 105 maypre-select or default select the highest result only and generate a GUIwith the pre-selected top result. In such scenarios, the likelihood thatthe top result is relevant for the user is greater that the probabilitythat other results are relevant for the user. Thus, to attempt tominimize user interactions with the GUI and prevent inaccurate entries,inventory search system 105 may select only one of the classificationresults in the GUI. In such embodiments, inventory search system 105 maypre-select only an interactive icon corresponding to the first resultwith highest probability when the differences of correspondingprobability scores between top results is not within the range.

Process 1300 may be used to generate GUIs like the ones presented inFIGS. 16-18 to adjust the display for client devices 150. As previouslydiscussed, these processes may improve the technical field of generatingGUIs by automatically selecting the most relevant information andpresenting the highest relevant information to the client whilecomplying with display and visualization preferences restrictions.

FIG. 14 is an exemplary process flow illustrating a communicationsequence, consistent with disclosed embodiments. As shown in FIG. 14 ,communication sequence 1400 may involve multiple elements of system 100,including client devices 150, inventory search system 105, and onlineresources 140. However, other elements of system 100 may be involved incommunication sequence 1400. For example, databases 180 may perform someof the steps of communication sequence 1400.

In step 1402, client devices 150 may transmit search queries toinventory search system 105. In some embodiments the query sent in step1402 may include user-selected options. For example, the query mayinclude options a user selects in a GUI displayed in client devices 150,such as the GUIs shown in FIG. 16 . Further, the query may include useraccount information and/or location information of client devices 150.The account information may be based on login credentials to vehicleidentification application 514 (FIG. 5 ). In some embodiments, totransmit the query client devices 150 may determine a location ofcorresponding client devices, transmit a search query to a server.Further, the query may include identified attributes in a sample image,options selected with interactive icons, user input in input icons,and/or the location.

In step 1404, client devices 150 may query for inventory of items basedon the query received in step 1402. For example, if the query in step1402 asks inventory search system 105 to look for “black 2019 BMW inWashington D.C.” inventory search system 105 may transmit an inventorquery in step 1404. The inventory query may be an HTTP query, but othercommunication protocols are also possible. As shown in FIG. 14 , theinventory query may be transmitted to online resources 140. For example,the inventory query may be transmitted to online resources 140A,associated with a vehicle dealer, vehicle manufacturer, or aggregatorwebsites. Alternatively, however, the inventory query may be transmittedto databases 180, which may store up to date inventory information.

In step 1406, online resources 140 may respond to inventory searchsystem 105 query and return available items in the query that match thequery requirements. For example, online resources 140 may return a listof vehicles that match the query requirements of “black 2019 BMW inWashington D.C.” The response may include images of the items, such asthumbnail images, and characteristics of the items available, such astheir make, model, and year. In some embodiments, online resources 140may be configurable to send similar results in step 1406, even if theydo not comply with the query requirements. For example, online resources140 may return 2018 vehicles in addition to 2019 vehicles.

In step 1408, inventory search system 105 may perform a second query,this time requesting financing availability. The request may be sent toonline resources 140. For example, the request may be transmitted toonline resources 1408, which may be associated with financing services.The query sent in step 1408 may include the items received in step 1406that match the user selections. Further, the query may also includepotential seller and potential buyer information. For example, the queryfor online resources 1408 may include information of the user of clientdevices 150 that generated the query in step 1402 and information of thedealers offering the items.

In step 1410, online resources 140 may respond to the query withfinancing availability and estimated payments. For example, onlineresources 1408, associated with the finance service, may returninformation of whether the finance service can provide financing forpurchasing items in the inventory list and tentative payment plans.

In step 1412, inventory search system 105 may send matching items withfinancing availability to client devices 150 that generated the query.With this information, client devices 150 may generate GUIs that displayinventory search results. For example, client devices 150 may useinformation received in step 1412 to generate GUIs like the onepresented in FIG. 18 , showing secondary or inventory results after userselections.

In step 1414, client devices 150 may send a notification of selection.For example, a user of client devices 150 may select an interactive iconassociated with one of the inventory results (e.g., vehicles that areavailable in the inventory). Then, in step 1414 client devices 150 maysend a notification to inventory search system 105 that the userselected one of the inventory or secondary results.

In step 1416, inventory search system 105 may send a qualificationnotice. The qualification notice may include a tentative approval byfinance service, the availability of the selected vehicle based ondealer inventory information. Client devices 150 may use the informationreceived in step 1416 to generate graphical user interfaces, asdisplayed in FIG. 19 .

Referring now to FIG. 15 , there is shown an exemplary GUI 1500,consistent with disclosed embodiments. In some embodiments, graphicaluser interfaces may be displayed in client devices 150 and may bedisplayed during video streams of augmented reality applications,modifying images captured by camera 520.

In some embodiments, GUI 1500 may be displayed when a user opens anapplication that activates camera 520 in client device 150. For example,when a user opens vehicle identification application 514, client devices150 may be configured to generate GUI 1500 as shown in FIG. 15 .

GUI 1500 may include a background image 1502. The background image maybea ‘live’ image (i.e., an image being directly fed from camera 520). Inother embodiments, however, background image 1502 may be static image,previously captured and stored.

Background image 1502 may be overlaid with information icon 1504. Usingimage classification processes, such as process 600, client devices 150may learn attributes of background image 1502. For example, when thereis a vehicle in background image 1502, client devices 150 may learnattributes such as make, model, and trim using identification models(e.g., convolutional neural networks). Client devices 150 may use thatidentification information to create information icon 1504 and overlayit on background image 1502. Information icon 1504 may include vehicleattributes such as make, model, trim, and also inventory information(i.e., item availability) and financing options (e.g., price point).

GUI 1500 may also include a banner 1508 that includes additionalinformation for the vehicle identified in background image 1502. Banner1508 may include buttons 1506 to display additional similar vehicles, athumbnail 1510 displaying an inventory image of identified vehicles, andadditional information 1512. Additional information 1512 may include thesame information displayed in information icon 1504 or information withadditional details. For example, additional information 1512 may alsoinclude dealer information and tentative payment installments.

While banner 1508 in FIG. 15 shows a single item, in some embodimentsbanner 1508 may include a list of items. The items displayed may havesome similarity with attributes extracted from background image 1502 andmay be selected based on processes to improve user experience as furtherdescribed in connection with FIG. 13 .

Referring now to FIG. 16 , there is shown an exemplary GUI 1600,consistent with disclosed embodiments. In some embodiments, GUI 1600 maybe displayed in client devices 150 and may be displayed once a usercaptures a sample image to be searched. For example, GUI 1600 may bedisplayed after a user captures an image using camera 520.

GUI 1600 may include a title 1602 describing that a scan of an image wassuccessful and that preliminary results are presented below. Thepreliminary results may be identified using process 600 and useidentification models generated by model generator 120 (FIG. 3 ).Preliminary results, or a subset of preliminary results, may bepresented as interactive result icons 1608. Interactive result icons1608 may be buttons, graphical selectors, and/or check boxes. In someembodiments, interactive result icons 1608 may display thumbnail imagesof results. For example, interactive result icons 1608 may includethumbnails of vehicles identified as preliminary results in process 600.Alternatively, or additionally, interactive result icons 1608 mayinclude attributes of the results. For example, as described in FIG. 16, interactive result icons 1608 may display vehicle attributes for theresults such as Make, Model, and Trim Line. In some embodiments, GUI1600 may include a subtitle 1606 that is based on the attributesdisplayed in interactive result icons 1608.

In some embodiments, interactive result icons 1608 may be programmed tothang appearance when selected. For example, interactive result icons1608 may be programmable objects that change color, transparency, size,font, and/or general appearance when selected. In such embodiments, forexample, interactive result icons 1608 may be programmed, not only totransmit a selection, but also to execute scripts such as:

  [buttonName setTitleColor:[UIColor blackColor]forState;UIControlStateNormal];buttonName.setTitleColor(UIColor.blackColor(), forState: .Normal)buttonName.setTitleColor(UIColor.white, for: .normal).

GUI 1600 may also include an “inaccurate” button 1610 and/or a“Repopulate” button 1612. As further described in connection to FIG. 12, when a user is unsatisfied with the preliminary results shown ininteractive result icons 1608, the user many notify a server that theresults are inaccurate and or request additional results to be displayedin GUI 1600. “Inaccurate” button 1610 may notify a server that thepresented preliminary results are not relevant. In some embodiments,selecting Inaccurate” button 1610 may result in the generation of afeedback message to, for example, inventory search system 105. In turn,inventory search system 105 may update the training data set andgenerate a patch, as described in process 1200 (FIG. 12 ).Alternatively, or additionally, a user may select “Repopulate” button1612 to request additional results to be displayed in GUI 1600, asfurther described in connection with process 1300 (FIG. 13 ). Further,client devices 150 may be configured to modify the results icons bydisplaying a second subset of the first results when receiving aselection of the repopulate icon, the second subset being different formthe first subset.

GUI 1600 may also include one or more options or filter elements thatmay be displayed in GUI 1600 as filter icons. Filter icons may berelated to option labels 1626 and may include slider icons, buttons,selection boxes, or general selectors. As shown in FIG. 16 , GUI 1600may include a slider icon 1616 to filter results based on year, or yearrange. Slider icon 1616 may be associated with label 1626A. In someembodiments, however, slider icon 1616 may be substituted with acalendar icon, a text field, or similar date-related selection. GUI 1600may also include style filter icons 1618. Style filter icons may beassociated with label 1626B and include buttons or selectors. In someembodiments, style filter icons 1618 may be programmed to changeappearance when selected. Similarly, GUI 1600 may include color filtericons 1620, associated with label 1626C, and features filter icons 1622,associated with label 1626D.

As shown in FIG. 16 some of the icons may be preselected and may have achanged appearance as soon as the GUI is created. For example, colorfilter icons 1620 may be programmed with the following operations tochange color, but transparency, contrast, size, font, or otherattributes may also be manipulated to change appearance:

  let origImage = UIImage(named: ″imageName″); let tintedimage =origimage_RenderingMode(.alwaysTemplate); btn.setimage(tintedimage,forState: .normal);  btn.tintColor = redColor; functiondeneme_deneme_OpeningFcn(hObject, eventdata, handles, varargin);

Other processes to change dynamically icon appearance may include:

  jFrame=get(handles.figure1,'javaframe');jicon=javax.swing.ImageIcon('icon.gif'); jFrame.setFigureIcon(jicon);handles.output = hObject;  guidata(hObject, handles);

As previously disclosed in connection with FIG. 7 , selection ofinteractive result icons 1608, features filter icons 1622, or colorfilter icons 1620, may result in a dynamic change of GUI 1600. Forexample, depending on the selection of interactive result icons 1608some of the filter icons 1616-1622, may be eliminated or added based onthe availability of the options for the selected result. For example,client devices 150 may modify the appearance of GUI 1600 based on userselections using methods such as “repaint( ),” “revalidate( ),” and“dolayout( )”.

Further, as previously discussed in connection with FIG. 13 , based onresults confidence levels and/or visualization preferences, some of thefilter icons in GUI 1600 or the interactive result icons 1608 may bepreselected to facilitate user navigation an minimize opportunities ofinaccurate selections, improving the technical field of generating GUIs.For example, based on the model output, different results may bedisplayed in GUI 1600 and inventory search system 105 may determine howmany results to show, whether to auto-select results (based on athreshold of the top results and their confidence score similarity). Insuch embodiment, the auto-selected results may be based on the specificoutput of the models. If the model predicts make/model, the autoselection can be based on make/model. But if the model predictsmake/model/Year/trim, body style, and color, the default auto-selection(or pre-selection) may be based on these parameters.

Moreover, GUI 1600 may include a search button 1624. Search button 1624may be an object programmed to generate queries. For example, when auser selects search button 1624, client devices 150 may transmit queriesfor inventory search results in step 1402 (FIG. 14 ).

Further, in generating GUI 1600 inventory search system 105 maycustomize icons and preliminary results displayed in GUI 1600. Forexample, depending on sample images, user selections of characteristics,and image classification or prediction model results, inventory searchsystem 105 may query a lookup table or perform a search to know whattrim, year, body style, colors, features, etc. are available for theidentified or selected combinations. In some embodiments, based on themake and model identified for a vehicle, inventory search system 105 maypopulate GUI 1600. Then, a user may choose to default multiselect thetop two make/model predictions, icons 1618 and labels 1626 may bepopulated based on those and available options. In some embodiments, asfurther described in connection to FIG. 6 , the availability may bedetermine based on area, specific dealership, or within a certainradius, or what is available from the manufacturer.

As discussed in connection to FIG. 7 , icons 1618 may be dynamic. Forinstance, if a user changes the selection of make/model, labels 1626 andicons 1618 may get repopulated. In such embodiments, the updates to GUI1600 may be based on auto or pre-selected options, which may be based onpopularity, user defined preferences, or confidence values. Further, ifinventory search system 105 employees a model predicting make/model/trimand color, those specific characteristics may be auto-selected based onthe model outputs as described in connection to FIGS. 6-7 .

Referring now to FIG. 17 , there is shown an exemplary GUI 1700,consistent with disclosed embodiments. In some embodiments, GUI 1700 maybe displayed in client devices 150 and may be displayed once a usercaptures a sample image to be searched. For example, GUI 1700 may bedisplayed after a user captures an image using camera 520 (FIG. 5 ).

Similar to GUI 1600, GUI 1700 may include a title 1702 describing that ascan of an image was successful and that preliminary results arepresented below. Further, GUI 1700 may include interactive result icons1708 that are similar to interactive result icons 1608 presented in FIG.16 , including animations to change appearance of icons when they areselected and thumbnails or attributes of the identified results. In someembodiments, interactive result icons 1708 may be configured asgraphical objects that may transmit messages to a server or change textin buttons when selected. For example, interactive result icons 1708 mayperform scripts such as:

  function change() // no ';' here { var elem =document.getElementById(″myButton1″);  if (elem.value == ″SelectedModel″) elem.value = ″Open Curtain″  else elem.value = ″Not selectedmodel″:}

Moreover, GUI 1700 may also include a repopulate icon, which may beconfigured to triggered operations similar to the ones described in forrepopulate icon 1612 and process 1300.

GUI 1700 may also include a “Clear” button 1710. “Clear” button 1710 maybe configured to remove interactive result icons 1708. For example, if auser decides that the preliminary results are irrelevant and wants tofocus on other elements of the GUI, the user may select “Clear” button1710 which may delete interactive result icons 1708 with operations suchas:

  Gui +LastFound; WINCA NODRAWICON := 2; WTNCA NOSYSMENU := 4;DIICall(″uxtheme\SetWindowThemeAttribute″, ″ptr″, WinExist()  ,″int″ 1,″Int64*″, 6 | 6<<32, ″uint″, 8); Gui Show, W200 H100,

Furthermore, GUI 1700 may include fields for user input and to providefeedback and address inaccuracies in the image classification and/orclassification model. Thus, GUI 1700 may include a subtitle 1714indicating GUI elements to provide feedback. Feedback elements 1718 mayinclude text fields (1718A-1718C) that may capture user input.Alternatively, feedback elements 1718 may include dropdown menus,selectors, or other interactive icons that enable the user to inputinformation. In some embodiments, feedback elements 1718 may be adaptedbased on the user input. For example, when feedback elements 1718 aretext fields, the text fields may include predictive auto-fill options.

Feedback elements 1718 may be specific for certain aspects of imagerecognition. For example, as shown in FIG. 17 , feedback element 1718Amay be dedicated to receiving feedback for vehicle Make, and may beassociated with a label 1716A that displays the type of feedback thatcan be entered in feedback element 1718A. Similarly, feedback element1718B may be dedicated to receiving feedback for vehicle Model and maybe associated with a label 1716A. Further, feedback element 1718C may bededicated to receiving feedback for vehicle Trim and may be associatedwith a label 1716C. Other feedback elements 1718 for different aspectsor attributes may also be displayed in GUI 1700. For example, feedbackelements 1718 for attributes such as color, features, and/or body stylemay also be displayed in GUI 1700.

GUI 1700 may also include a “Send Feedback” button 1720. “Send Feedback”button 1720 may be an object programed to generate feedback messages toa server. For example, as further discussed in connection to process1200 (FIG. 12 ), when a user selects “Send Feedback” button 1720 thecaptured image may be labeled and sent to inventory search system 105 toinclude the image as part of the training dataset and update the modelto attempt to correct inaccuracies. Alternatively, or additionally, insome embodiments when a user selects “Send Feedback” button 1720, clientdevices 150 may perform operations of determining whether the at leastone input icon is empty, transmitting, to a server in system 100, thecaptured image and input in the at least one input icon when determiningthe at least one input icon is not empty. When the feedback elements1718 are not empty, it may indicate that the user is sending acorrection of the image classification results. Thus, selection of “SendFeedback” button 1720, or a “Show Inventory” button 1722 (describedbelow), may result in transmission of corrected data. In suchembodiments, client devices 150 or inventory search system 105 may beconfigured to receive, from the server, a patch for the classificationmodel, which may include updated model parameters. This process mayfacilitate correcting models and improve the model performance.

Alternatively, or additionally, GUI 1700 may include a “Show Inventory”button 1722. When selected, “Show Inventory” button 1722 may send aquery for secondary results without any selected options or anyfiltering conditions. For example, when a user decides that interactiveresult icons 1708 are not relevant, a user may select to review allelements in the inventory by selecting “Show Inventory” button 1722. Insome embodiments, “Show Inventory” button 1722 may generate a transmit asearch query to online resources 140, instead of inventory search system105, to directly get information from dealers or automobilemanufacturers without an intermediary to minimize network congestion.

Referring now to FIG. 18 , there is shown an exemplary GUI 1800,consistent with disclosed embodiments. In some embodiments, GUI 1800 maybe displayed in client devices 150 and may be displayed once a userselects an interactive icon that generates a query for secondary resultsor inventory results. For example, GUI 1800 may be displayed after auser selects search button 1624 (FIG. 16 ).

GUI 1800 may include a list of results that may be composed of availableitems that match selections by the user in previous GUIs. For example,GUI 1800 may display inventory or secondary results that match optionsor preliminary results selected in GUI 1600. GUI may display secondaryresults 1802 (e.g., 1802A-1802B). Each of the secondary results 1802 mayinclude an image 1804 (e.g., 1804A-1804B) and a condition 1806 (e.g.,1806A-1806B). Further, each of the secondary results 1802 may display adistance and condition 1806, based on inventory results and clientdevices 150 location, a vehicle description 1808 (e.g., 1808A-1808B), aprice 1810 (e.g., 1810A-1810B), and financing options 1812 (e.g.,1812A-1812B). As described in connection to FIGS. 10 and 14 , theinventory queries may be complemented with queries to online resources140 that indicate availability of financing options.

As shown in FIG. 18 , multiple secondary or inventory results may bedisplayed in GUI 1800 in the form of a list. However, in otherembodiments secondary results 1802 may be displayed in a carousel viewor in mosaics based on display availability and/or user preferences.Further, GUI 1800 may also include “Search,” “Filter,” or “Sort” iconsto help navigate the results obtained. In addition, GUI 1800 may include“Save” buttons for each of secondary results 1802, which may storeresults information in local memories, such as memory 510.

Referring now to FIG. 19 , there is shown an exemplary GUI 1900,consistent with disclosed embodiments. In some embodiments, GUI 1900 maybe displayed in client devices 150 and may be displayed once a userselects a result of an item he is interested in. For example, GUI 1900may be displayed after a user selects secondary results 1802.

GUI 1900 may include a notice 1902 and a message 1904. Further, GUI 1900may include buttons for connecting with online resources 140. Forexample, next steps button 1910 may open websites or resourcesassociated with online resources 140 associated with dealers orfinancial services. Further, GUI 1900 may include a “Find Cars” button1908 which may display additional results or connect client devices 150with dealers or automobile manufacturers. For example, when selectingfind “Find Cars” button 1908, client devices 150 may be configured toconnect to a dealer website that carries selected vehicles of interestto the user.

Referring now to FIG. 20 , there is shown an exemplary GUI 2000,consistent with disclosed embodiments. In some embodiments, GUI 2000 maybe displayed in client devices 150 and may be displayed once a usercaptures an image. For example, GUI 2000 may be displayed after a usercaptures an image using camera 520.

GUI 2000 may be displayed when the sample image the user captured forimage classification is not acceptable for identification. For example,GUI 2000 may be displayed in step 608 (FIG. 6 ). An image may not beacceptable for identification because it does not have a necessaryformat or quality to extract image attributes, as described in process600.

GUI 2000 may include a notice 2002 and a message 2004. Further, GUI 2000may include a “Back” button 2006. In some embodiments, when a userselects “Back” button 2006, client devices 150 may reopen an augmentedreality application or an application for camera 520 to recapture animage.

Another aspect of the disclosure is directed to a non-transitorycomputer-readable medium storing instructions that, when executed, causeone or more processors to perform the methods, as discussed above. Thecomputer-readable medium may include volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other typesof computer-readable medium or computer-readable storage devices. Forexample, the computer-readable medium may be the storage unit or thememory module having the computer instructions stored thereon, asdisclosed. In some embodiments, the computer-readable medium may be adisc or a flash drive having the computer instructions stored thereon.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed system andrelated methods. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice of thedisclosed system and related methods. It is intended that thespecification and examples be considered as exemplary only, with a truescope being indicated by the following claims and their equivalents.

Moreover, while illustrative embodiments have been described herein, thescope thereof includes any and all embodiments having equivalentelements, modifications, omissions, combinations (e.g., of aspectsacross various embodiments), adaptations and/or alterations as would beappreciated by those in the art based on the present disclosure. Forexample, the number and orientation of components shown in the exemplarysystems may be modified. Further, with respect to the exemplary methodsillustrated in the attached drawings, the order and sequence of stepsmay be modified, and steps may be added or deleted. Furthermore, whilesome of the exemplary embodiments of the computerized methods weredescribed using Java language or C to illustrate exemplary scripts androutines, the disclosed methods and systems may be implemented usingalternative languages. The disclosed embodiments may use one or multipleprogramming languages in addition to Java or C. For example, thedisclosed embodiments may also be implemented using Python, C++, C#, R,Go, Swift, Ruby, and/or their combinations.

Thus, the foregoing description has been presented for purposes ofillustration only. It is not exhaustive and is not limiting to theprecise forms or embodiments disclosed. Modifications and adaptationswill be apparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments.

The claims are to be interpreted broadly based on the language employedin the claims and not limited to examples described in the presentspecification, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods may be modified in anymanner, including by reordering steps and/or inserting or deletingsteps.

What is claimed is:
 1. A system for generating and implementing patchesto improve classification model results based on user feedback throughinput icons, the system comprising: a camera; one or more processors;and one or more memory devices storing instructions that, when executedby the one or more processors, configure the one or more processors toperform operations comprising: capturing an image with the camera;generating a first graphical user interface comprising one or more firsticons corresponding to first results, the first results comprisingobject recognition results based on attributes identified in the imageusing a classification model, wherein the classification model comprisesmodel hyperparameters; and upon receiving a user selection of at leastone of the first icons: performing a search to identify second results,the search being based on the selected at least one of the first icons;generating a second graphical user interface displaying the secondresults, the second graphical user interface being different from thefirst graphical user interface; receiving, from a server and based onthe second results, a patch for the classification model, the patchcomprising updated model hyperparameters and a classification modelexception for the identified attributes, and wherein the patch includesa script that modifies a response of the classification model to imageswith attributes; based on the patch, retraining the classification modelto include the updated model hyperparameters such that the response ofthe classification model to images with attributes is modified, whereinretraining the classification model to include the updated modelhyperparameters comprises developing the classification model based on atraining dataset; and performing a conditional routine to substitutethird results based on the classification model exception.
 2. A methodfor generating and implementing patches to improve classification modelresults based on user feedback through input icons, the methodcomprising: capturing an image with a camera; generating a firstgraphical user interface comprising one or more icons corresponding tofirst results, the first results comprising object recognition resultsbased on attributes identified in the image using a classificationmodel, wherein the classification model comprises model hyperparameters;and upon receiving a user selection of at least one of the icons:performing a search to identify second results, the search being basedon the selected at least one of the icons; generating a second graphicaluser interface displaying the second results, the second graphical userinterface being different from the first graphical user interface;receiving, from a server and based on the second results, a patch forthe classification model, the patch comprising updated modelhyperparameters and a classification model exception for the identifiedattributes, and wherein the patch includes a script that modifies aresponse of the classification model to images with attributes; based onthe patch, retraining the classification model to include the updatedmodel hyperparameters such that the response of the classification modelto images with attributes is modified, wherein retraining theclassification model to include the updated model hyperparameterscomprises developing the classification model based on a trainingdataset; and performing a conditional routine to substitute thirdresults based on the classification model exception.
 3. The method ofclaim 2, wherein the classification model comprises a convolutionalneural network.
 4. The method of claim 3, wherein the patch comprisesupdates for connection layers of the convolutional neural network. 5.The method of claim 2, wherein the second graphical user interfacecomprises: images associated with the second results; conditionsassociated with the second results; and distances associated with thesecond results.
 6. The method of claim 2, further comprising, uponreceiving a user selection of a first button of the first graphical userinterface: determining whether an input icon of the first graphical userinterface is empty; and in response to determining the input icon is notempty, transmitting, to a server, the image and content in the inputicon.
 7. The method of claim 2, wherein the classification modelexception is based on content in the at least one of the icons.
 8. Themethod of claim 2, further comprising updating the classification modelby running the patch.
 9. The method of claim 2, wherein the patch isconfigured to automatically execute commands and invoke patch managementsystems in an operating system.
 10. The method of claim 2, wherein theicons display thumbnails of vehicles identified as preliminary results.11. The method of claim 10, wherein the icons are configured to changecolor and transparency when selected.
 12. The method of claim 2, furthercomprising upon receiving a user selection of a first button of thefirst graphical user interface, transmitting an error message to theserver.
 13. The method of claim 2, wherein generating the secondgraphical user interface comprises displaying the second results in aranking based on financing availability.
 14. The method of claim 2,further comprising: upon receiving a user selection of a first button ofthe first graphical user interface: transmitting a repopulate request tothe server; removing the one or more icons from the first graphical userinterface; and displaying second icons in the first graphical userinterface.
 15. The method of claim 14, further comprising: uponreceiving a user selection of a second button of the first graphicaluser interface: transmitting, to the server, a query for availablevehicles without filtering conditions.
 16. The method of claim 2,further comprising: upon receiving a user selection of a first button ofthe first graphical user interface, generating a third graphical userinterface displaying an augmenter reality application.
 17. The method ofclaim 2, wherein generating the first graphical user interfacecomprises: retrieving visualization preferences from a local memory; anddetermining the first results by truncating preliminary search resultsbased on the visualization preferences.
 18. The method of claim 2,wherein generating the first graphical user interface comprisespreselecting at least one of the one or more icons based on confidencelevels of the first results.
 19. The method of claim 18, whereinpreselected first icons are displayed in a different color in the firstgraphical user interface.
 20. One or more non-transitory,computer-readable media storing instructions that, when executed by oneor more processors, cause operations comprising: capturing an image witha camera; generating a first graphical user interface comprising one ormore icons corresponding to first results, the first results comprisingobject recognition results based on attributes identified in the imageusing a classification model, wherein the classification model comprisesmodel hyperparameters; and upon receiving a user selection of at leastone of the icons: performing a search to identify second results, thesearch being based on the selected at least one of the icons; generatinga second graphical user interface displaying the second results, thesecond graphical user interface being different from the first graphicaluser interface; receiving, from a server and based on the secondresults, a patch for the classification model, the patch comprisingupdated model hyperparameters and a classification model exception forthe identified attributes, and wherein the patch includes a script thatmodifies a response of the classification model to images withattributes; based on the patch, retraining the classification model toinclude the updated model hyperparameters such that the response of theclassification model to images with attributes is modified, whereinretraining the classification model to include the updated modelhyperparameters comprises developing the classification model based on atraining dataset; and performing a conditional routine to substitutethird results based on the classification model exception.